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    "<font face=\"Times New Roman\" size=5><div dir=rtl align=center>\n",
    "<font face=\"Times New Roman\" size=5>\n",
    "In The Name of God\n",
    "</font>\n",
    "<br> <br>\n",
    "<img src=\"https://logoyar.com/content/wp-content/uploads/2021/04/sharif-university-logo.png\" alt=\"University Logo\" width=\"150\" height=\"150\">\n",
    "<br>\n",
    "<font face=\"Times New Roman\" size=4 align=center>\n",
    "Sharif University of Technology - Department of Computer\n",
    "</font>\n",
    "<br> <br>\n",
    "<font color=\"#008080\" size=5>\n",
    "Introduction to Machine Learning\n",
    "</font>\n",
    "\n",
    "<hr/> <br>\n",
    "<font color=\"#800080\" size=6>\n",
    "Chapter 3: Bagging & Boosting\n",
    "<br>\n",
    "</font>\n",
    "<br>\n",
    "<font face=\"Times New Roman\" size=4>\n",
    ":authors <br>\n",
    "<b>OmidReza Heydari - Seyyed Alireza Ghazanfari</b>\n",
    "</font>\n",
    "<hr>\n",
    "</div></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i6NrAXONPtcR"
   },
   "source": [
    "<font  size=5><div dir=ltr>\n",
    "<font face=\"Times New Roman\" color=\"#008080\" size=5>**Table of Contents**</font>\n",
    "<br>\n",
    "\n",
    "<font size=4>\n",
    "<ul>\n",
    "        <li>\n",
    "            <a href=\"#0\">\n",
    "                0. Dataset\n",
    "            </a>\n",
    "        </li>\n",
    "        <li>\n",
    "            <a href=\"#1\">\n",
    "                1. Majority Voting\n",
    "            </a>\n",
    "        </li>\n",
    "        <li>\n",
    "            <a href=\"#2\">\n",
    "                2. Bagging\n",
    "            </a>\n",
    "        </li>\n",
    "        <ul>\n",
    "            <li>\n",
    "                <a href=\"#2-1\">\n",
    "                    2-1. Out Of Bag Validation\n",
    "                </a>\n",
    "            </li>\n",
    "        </ul>\n",
    "        <li>\n",
    "            <a href=\"#3\">\n",
    "                3. Pasting\n",
    "            </a>\n",
    "        </li>\n",
    "        <li>\n",
    "            <a href=\"#4\">\n",
    "                4. Random Forest\n",
    "            </a>\n",
    "        </li>\n",
    "        <li>\n",
    "            <a href=\"#5\">\n",
    "                5. Extra-Trees\n",
    "            </a>\n",
    "        </li>\n",
    "        <li>\n",
    "            <a href=\"#6\">\n",
    "                6. Boosting\n",
    "            </a>\n",
    "        </li>\n",
    "        <ul>\n",
    "            <li>\n",
    "                <a href=\"#6-1\">\n",
    "                    6-1. AdaBoost\n",
    "                </a>\n",
    "            </li>\n",
    "            <ul>\n",
    "                <li>\n",
    "                    <a href=\"#6-1-1\">\n",
    "                        6-1-1. SAMME\n",
    "                    </a>\n",
    "                </li>\n",
    "                <li>\n",
    "                    <a href=\"#6-1-2\">\n",
    "                        6-1-2. SAMME.R\n",
    "                    </a>\n",
    "                </li>\n",
    "            </ul>\n",
    "            <li>\n",
    "                <a href=\"#6-2\">\n",
    "                    6-2. Gradient Boost\n",
    "                </a>\n",
    "            </li>\n",
    "            <ul>\n",
    "                <li>\n",
    "                    <a href=\"#6-2-1\">\n",
    "                        6-2-1. Without scikit-learn\n",
    "                    </a>\n",
    "                </li>\n",
    "                <li>\n",
    "                    <a href=\"#6-2-2\">\n",
    "                        6-2-2. With scikit-learn\n",
    "                    </a>\n",
    "                </li>\n",
    "            </ul>\n",
    "            <li>\n",
    "                <a href=\"#6-3\">\n",
    "                    6-3. Early Stopping\n",
    "                </a>\n",
    "            </li>\n",
    "            <ul>\n",
    "                <li>\n",
    "                    <a href=\"#6-3-1\">\n",
    "                        6-3-1. Method\n",
    "                    </a>\n",
    "                </li>\n",
    "                <li>\n",
    "                    <a href=\"#6-3-2\">\n",
    "                        6-3-2. Find Min Validation Error\n",
    "                    </a>\n",
    "                </li>\n",
    "            </ul>\n",
    "            <li>\n",
    "                <a href=\"#6-4\">\n",
    "                    6-4. XGBoost\n",
    "                </a>\n",
    "            </li>\n",
    "            <li>\n",
    "                <a href=\"#6-5\">\n",
    "                    6-5. CatBoost\n",
    "                </a>\n",
    "            </li>\n",
    "            <ul>\n",
    "                <li>\n",
    "                    <a href=\"#6-5-1\">\n",
    "                        6-5-1. Categorical Features\n",
    "                    </a>\n",
    "                </li>\n",
    "            </ul>\n",
    "        </ul>\n",
    "    <li>\n",
    "                <a href=\"#7\">\n",
    "                    7. Stacking\n",
    "                </a>\n",
    "            </li>\n",
    "            <ul>\n",
    "                <li>\n",
    "                    <a href=\"#7-1\">\n",
    "                        7-1. Multi-layer Stacking\n",
    "                    </a>\n",
    "                </li>\n",
    "            </ul>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
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   "metadata": {
    "id": "sBzocXtIiTbu"
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   "source": [
    "<font face=\"Times New Roman\"><div id=\"0\">\n",
    "# <font color=\"#800080\" size=6>**0. Dataset**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "swwrxuSMAESj"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Dataset**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ju9aCx5yAESj"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Introduction to Dataset </font>\n",
    "\n",
    "<font face=\"Times New Roman\" size=3>\n",
    "The dataset of this section belongs to a marketing campaign of a bank in Portugal. You can find more information about this dataset in this <a href=\"\">link</a>. This dataset is used for all next methodologies.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gYuiA7fFAESk"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Loading Dataset </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "height": 143
    },
    "id": "FybchF26AESk",
    "outputId": "67e844e3-408c-4de9-a172-6c55a96f8f5d"
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      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome Target  \n",
       "0  unknown    5   may       261         1     -1         0  unknown     no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown     no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown     no  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"../Files/bank-full.csv\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "helJaWTOAESk"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - More Information About Dataset </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "height": 300
    },
    "id": "O-0q0QBrrDuM",
    "outputId": "2e44a2d7-4c3d-47bc-ca29-1dcae08f8336"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "\n",
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       "    async function quickchart(key) {\n",
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       "            'suggestCharts', [key], {});\n",
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       "    })();\n",
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      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5QDfSkx7rDuS",
    "outputId": "0ec0edb8-50cb-4a54-c996-6036ef23a74c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   default    45211 non-null  object\n",
      " 5   balance    45211 non-null  int64 \n",
      " 6   housing    45211 non-null  object\n",
      " 7   loan       45211 non-null  object\n",
      " 8   contact    45211 non-null  object\n",
      " 9   day        45211 non-null  int64 \n",
      " 10  month      45211 non-null  object\n",
      " 11  duration   45211 non-null  int64 \n",
      " 12  campaign   45211 non-null  int64 \n",
      " 13  pdays      45211 non-null  int64 \n",
      " 14  previous   45211 non-null  int64 \n",
      " 15  poutcome   45211 non-null  object\n",
      " 16  Target     45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Kzb9jNlrDuU"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Visualizing Dataset </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0BrjWqO-AESk"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Pre-Processing Dataset</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "-mpV4390AESl"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# First of all we should change non-numerical values to numbers\n",
    "encoder = LabelEncoder()\n",
    "columns_to_encode = ['job', 'marital', 'education', 'month', 'contact', 'poutcome', 'housing', 'loan', 'Target', 'default']\n",
    "for column_to_encode in columns_to_encode:\n",
    "    encoder.fit(np.array(df[column_to_encode]))\n",
    "    df[column_to_encode]=encoder.transform(df[column_to_encode])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "MgjRV6qRrDuZ"
   },
   "outputs": [],
   "source": [
    "# Next step is removing unknown rows\n",
    "colums_have_unknow=['job', 'marital', 'education', 'default', 'housing', 'loan']\n",
    "for colum_has_unknow in colums_have_unknow:\n",
    "    df.drop(df[df[colum_has_unknow] == 'unknow'].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "n0Ei0F6YrDua",
    "outputId": "0ea1b33e-ca42-48d2-9584-62ecd2342f62"
   },
   "outputs": [
    {
     "data": {
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8GQKjFhixsLD8eO63YcpaM8Z0NWD5yBA1vRcWeqb3GjM2HbFj2iATFjwerKb3V4Vej/fGt0VIrO79gvyFJYXYnubA/91nwg/PhuC9B4JQ7zIDVOVZvd+OKWstGNNVj+Ujg5BYXcKIzwu9phEA7D7pwLivzXigtRYrRgWhZ2MNnvlKHai+ZPZWGxbusGFiHwOWjAhSy8zCIq/n8J11VsSElL2cOZ0tY8yXRWhXT8I3o4Ix+1ETsosUPLe46KrjWhGzt1iwMMWCifcFY8noMDX95+WVn/77rJiyuhBjuhuxfEyYmv6l++f5Ml65KwgrnwvH5AHB2HLEhteX+0j/5QVIrHH1aVqeQKW3zangrmYaDG7rvX0SBQE9GmvxyUMmrHk2GJP7GZFyzIkJ31deOV99wI4pP1oxposey58yqem0qAhZhT7ifsqJccsseKCVFiueMqnt1mIzjqSXiPs2Gxb+asPEe/RYMtyk1ouLimB1qHFvVVvClrFBHp+BrbSICxfQPNYz39udCsYtN+OWOv5J+0sC1X4DwHNdtNjygtH1ebSt/2/ME1VFVzUI2Lx5cxw5cgSdOnXCfffdh8LCQvTv3x979uxBgwYNKjuMfnHg57X49sOJSF3395n9d4miKFiwzYJRXQ3o0USHxBoaTH0gCOn5Mtb/YfO537ytFgy8RY8BbfRIiJEwqa8JBi2wbJdayTaqrsH0h0PQvbEOdSIltG+gxYt3mLDpkB0Op1qxD2ijx+v3BOHWelrUriahb7Ie/Vvrse6A79+9nhRFwYJlqRj1aFv0uK0+EhtEYeqrdyA9sxDrfzlW7r6FZhte+s+P+Ne4bggNuXFmNpamKAoWbMrDqF7h6JFkQmItHaYOiUZ6rgPr9/ru3N7ezIQX7o3AHaVm/5V0363BGHN3ODokBj7+/srnADC0owFPdTGiZW3vF73ZhTJOZMl48nYDEmtoEB8lYeydJpjtwJ8X/DtQNG8PMLA50L+pgIRIARO6qxfpyw96335hKtCpLjC8jYAG1QQ810FA02hg0V7130/kAHvPA292A1pUF1AvQsCEboDVAaw+7D7OnvPAI0nqjMPaYQJG3SogRA8cTPdrdH1SFAUL1qZjVN/q6NEmHIl1jJg6si7Sc+xYvzvX5363twzDCw/E4o5bwr3++/HzVuw9WoQJQ2ujRf0g1K9pwMShtWGxKViVku2n2PimKAoWpFgwqkuJfD7gUj63+9xv3rbifN66OJ/fW5zPd7vLxtCOBjx1uwEta3u/IHA4FfxndRFe7mXE4Fv1qBclISFGwt0t/D8IOG+HHQNbaTAgWYuEaBGTeutg0ApYluo9zgt/s6NTAwnDO+jQIErE8111aFpTxKKd6vYhBgFzHzHi7qYa1I8UkRwn4Z936XDgnIyzueqFhkNW8J8fbXi5hw6D22hRL1JEQrSIu5v6b/DLX+mbb1GwbLcN4+8yoX19LZrX0mDy/UHYc9KJ1FPqDa/V++1IrCFhTDcj6kZKuLWeFi/1MuKLHVYUWNU23aQTMLFvEAbdokeUl4GUSj0PW80Y1d2EHs30SKypwdQHQ5CeJ2P9wbIXeq7z8IsZA281YMAtBiRU12BSv2AYdAKW7fS88fjHWQc+22LGvx8oO/NlQFsjXu8bjFvr61A7UkLfVgb0b2PAuv2+f/ea4rndilG3G9CjsRaJNSRM7V+c3ofKS28rBrbRYUCr4vTuY1TTe0+J9N5jw/he6mz05rEaTO5nwp5T7vS+5Mtf1RmnT3QsO7Nvy592/HbCgf89EoSODbSIi5DQqrYGretcfRmYl2LFwNZaDGilKw67QS3Le3yU5R02dErQYPhtejSIlvB8dwOa1pSw6Fc1ruo5tGHU7Xr3ObzfiPR8BesPecb15z/t2HrUgVfuLBvX/WedkBXghe561KkmolmshCc66vDHeRl2p39ubrnyeVcjejQtLu8Dg6+g32JAQowGk+4L8t4/b1K6f25z9c8v+XKHOgPxiU6+b/5ci0Cl93PdDBjWQY9GMd7rqTCjgIfa6tCiloRa4SI61NfgobZa7DpZeX22eSk2Ne7JWiRES5h0j74CcZcwvKNOjXs3vdpu/WZ3x32HHaM669EjUYvE6hKm9jN4xF0nCYgOFl2fcKOADYcd6J+sLXPTY9omG+pHirirqX8HxgLRfl8SpPM8HybdTfxmh0og8HPTuuoeX1hYGF5//XUsWbIEq1evxv/93/+hZs2alRk28uF0toyMAgUdG7gr+RCDiKQ4TZnO4CU2h4IDZ50e+4iigA4NtD73AYB8i4xgveB6dMj7NgrCTDdGMTp9Lg8ZF4vQsY17FlBIsB5JTaoj9eD5cvd9a9pP6NouHh3b1PF3MK/J6SwHMvKc6NjYPVAXYhSRFK9H6vHKv6gJlOuZz0sLNwmoFyXi2z02FNkUOJwKFv9mQWSQgGblPO5wrWxOBQfTgfYlJrGJgoAOtYHUc973ST0HdCg16e22uurAn3pM9b/6EsEWBQE6Cdhd4pitagA//AnkWBTIioLVRxTYHEBbz/c/XTenM2zIyHWgYzP3RX2ISUJS/SCk/uV99kNF2Irvruu17uZPFAXotAJ2Hbn6414tdz53X4SHGISK5fMSM7euJp8fPOfEhTwFggDcPyMPnafm4MkF+R4zNPzB5lRw4JyMjvXcmVIUBHSIl5B6xvuMitTTntsDwG31JaSe9j0rNN+idvBCix8FO3hOxoX84vjOMqPzh0V48kuLazaCP/grfQ+cdcDuhMdx60dLiA0TXdvYHAr0Gs+22aAVYHWo+19Ppy/KyMiX0THBPcAcYhCRVFuL1BPlnIczDo99RFFAhwQtUk+4LzbNNgUvfZWHN+8LRnQFBzL91W9xpXf9UuldSyo/vc95Se/6mrLpXb90egsex/0r3YlPfrJg6v1B8DIpEhsP29E8VoM5W624/d1c9PooD1PXmmEpZ5ZaedS8KnsP+2nv9UjqKSc61i9VlhMk1/ansxXv5zBO8jhmZoGMf35nwdT7jTBoy0a2eawEQQCWp9rhlBXkWxR8t9eODvUlaMvp016LcvstJ8sr797yuQ6pJ30PHOdblDL987/SHfhkYxGmPhDsNf2vVSDT+0pdyJOx7g8H2tatnJs8Ptuter7bodTTTnSs5/n7tzVwn6vTOZfi7j6mq77wEfeNRxzIMSvon+w50Lc9zYE1B+14s7d/b+IHqv2+ZNY2O9q9V4j7Z5kxJ8UGhxy4p1WI/s6uqmbct2+f1+8FQYDBYECdOnXKfUGI1WqF1eo5WOGUFUg383uar0BG8ZT7yFLrwEQFi8jM914ZZhcpcMpAZLBQZp+0TO+djOxCGf/dZMGgtr7TcvdJO3743YaZj/lv3YkrkXFRnQkXGeG51k9UhAmZF31f4K/aeAQH/8zA1/8d5NfwVYaMPLVjEBni2aBGhUjIzAvM44z+cL3yuTeCIOCzx0MxZlE+2vwrG6IAVAsSMGtoCMIqeW2dknLM6uN5UaWWqoo0Acd8TFLLLFL/vaQoE5BZnN3rRQA1Q4APtgETuyswaoEFe4DzBUBGiSLxfm9g3A9Ax08BjajOPvzoHqBueGDq5YxcNb0iwzw7ulFhGmTmVDwtS6tf04DYSC3eX3oWkx6vDaNexPw1GTh/0Y6Mazju1cooUPNymXweJCDTx+NV7nxeumwISMuseB1w6qJ6/BkbLRh/txG1wkV8ttWCIXPzseb5UISb/JPXs4sUOBUgMqh0ORWQluU9zpkFStntgwRk+ngMy+pQ8O5GG+5pJiFYr+53Kkc91zN+tmP8HTrUChfw2XY7hiw0Y83TJoQbKz+v+yt9M/IVaCWUWesrMlhAZvGSBZ0aarEgRV0X8u7mWmQWKPhkk8W1//Xkrs/L1s2+z4Ps4zyISMtwl9XJ3xegVR0tejSr2Jp2u0/Y8cM+K2YOC7uSKFSIz/QOFpFZcLl2y0s8Mx2u43pPb/dxbQ4F474uxMt3GhEbLuJUdtm64FS2jF0nHdBpgI8HByG7SMGkVUXIKVIw+f4rXyPRVZZLp2uQ77oos0DxUh7c8fCZV0qUGUVR8No3Zgy+RZ35dTq7bB6KixAx5zETXlxqxoSVFjgVIDlOwqeP+GctSACu5UK8p//lyruX+jDDR54plPHfzWYMause8LE5FIxbXICX7w5CbLjkqt8rU6DS+0qM/boIGw85YHEA3Rpp8H99K2dQzN1ula3Ly4+7t3hdintxfeGlLfRVXyzbo86qqxHqDkd2kYLXvrXg7fsNrvbOXwLVfgPAY221aFpDnQ2557QT72+yIb1AwWt38KWkRFfqqgYBk5OTXVOQFUWtpEpOSdZqtXjwwQfxv//9DwZD2cp38uTJmDRpksd3rauJuCXSv+vR/F2tTLViwnfuq/XrMeBWYFEwcmE+GsRIeKa790cKjlxwYMznBRjTzYhODQOzJsPK9Ycx4f1Nrr9nTr73io9xLj0f/5nxM+a+3Q963Y2XB1f+VoAJX2a5/p45unoAQ+M/gcjnviiKgrdWFiIyWMSiESbotQK+3mnF6M/zsXR0mNf1h25UWknAR/coeGM90OFTQBLUmYOd6wIlu5gfpQB5VmDO/UCEAdhwTF0TcOEDChpF+X8gcOW2i5jw2SnX3zPH1ffL72g1Aj56rj7emHMS7Ub/DkkEOjQLwe1JoVDg/4GRlXutmPCd+7H9mY/6/wUcvly6gT6yiwG9mqmzUCb3D0KXd3Kx5oAdg8u5AXQjszsVvLBMvdE4sbc7Dq74dtKiVxO1rp98r4guHxVhzUEHBre59nbsRkrfTglavNzLiInfFWL8MkAnAaO7GrHzhAP+vue6co8FE1bku/72x4AbAGw8aMWOo3Ysf65i69geOe/AmAW5GNPDhE6Nrv2x95X7bJiwskR6X4cX6vjy3noLGkRL6NvSd7xkRZ1d8+6AIIQUz7B51WHE80uKMKGP9xl1N6KFO2wotAJPdfYd14x8daZgv5Za3NNCi0Krgo82WfH8kiLMHWLyun7klVqZasWEb93rcM4cElrO1pWjwCJj5IJ8NIiW8EwPd//8vR+L1PRP/nvW25XltV4GPNNFwfEsGe9vsGLKWgsm9PHPo9HX2/k8Gb8cdeKDBzyvrd/83oI+zbWVNusxkHy13wDweHt3G51YXYRWAiastmFcNx10mr9H3UV0o7iq2mLFihUYP348Xn75Zdx6660AgF9//RXvvfceJkyYAIfDgVdffRVvvPEG3n333TL7v/baaxg7dqzHd2NbR11NUG4K3ZrokFRi7bJLj7NlFcgegxGZBbLHW9FKijAJkEQgq9SdpcwCucyb5QqsCkbMz0eQTsDHDwd7fWzir3QnHp+bj0Ft9RjdLXCNa7eO9ZDUxD0oZit+9jEruwgxke617zKzi9AkIdrrMQ4cSUdWthn9R37l+s4pK9i57wwWfbMP+9Y+DUkK3KBPtxYmJMW7G0JX+uc7ERPmzheZ+U40ifv7viHreufz8mw/5sDmw3b8+nqE661yzfpqsO2oHd/stuKpLv7J8+FGdZAus9TSjllFZWcHXhJlUv+9pMwiIKrE0o/NYgSseBjItyqwO4FqJgEPLlbQPEb995M5Cr7YB3z7CNAwUo1v42hg11n1+4ndKymC5ejWKgxJDdyBthW/5CYr146YcHfHLzPXgSZ1r+38N69nwjf/1xj5RU7YHTKqhWoxaOJhNK/nvxkil3RrrPN44+el9xWVyeeFCpr4WNjdnc8976JnFihXlM+jQ9S0Tohx/45OI6B2NRHncvz3iGyESYAkoMwi4mr4vXfko4KFstsXKogqNSvD7lTw4nIrzuYqmPeo56yI6OJjJ0S599FpBNQOF3Eur3IGgK9X+kaHCLA7gTyz7DE7LKtA8Vjb7/HbDBjWUY/0fAVhRgFnsmW8v86M2n58OyoAdGuqQ1Jt90uobM5L9bmCmBLjJGp97r0rGmESfZwHd32+/agdJy86ceukTI9tnvs8D23itVg4Mtz13V8XHHh8dg4G3WrE6B6+18a9Et0StUiq5b5hdWn5Ba/t1hWnt+wqD9HBvtLbvc2ONDuOXJCx9mAOAKD4Hj06vJ2HkZ31eK67EdHBAqqHiq4BQABoEC1BUdQBhsu9wdhr2AUvbW6h77ooKlgoG9fCknEVi+OmICak5DbuMrMjzYnU004k/Svf4zgPfFqIPklaTL3fiC9+syHEALx8p3vA5J3+Irp+UIC9p51I9rEm8JVQ+y3hrr89+i2hpfstvvK5r35L2frQ1T/XC/j4kRCP/vmOo3YcueDE2n+qN41d6f+fbIzsYsRzPa+9fQtUel+J6BAR0SHq4/JhRgGPfFaE0V3013zz1t1uyQDc4bp83L2dK3e5BtS20CPuBQqa1Ch7zOWpdoQbBXRv5JmXtqc5sPEwMDeleJ1FqAP+zf6Vj7f6GDCgVeVN1AhU++1NUi0JDhk4naugfiQHAYmuxFW1gP/+978xbdo09OrVy/VdixYtEBcXh3/+85/49ddfERQUhHHjxnkdBNTr9WUeF+ajwL4F6wUEl1jQS1EURAcLSDlqd3UqCiwK9p124KFbvd8B1GkENIuVkHLMjp5N1YEiWVaw/Zgdj7Rzd5AKLAqGz8+DThLwyaMh0Hu5K/znBQeGzc1Hv1Z6vHiH/y+ayxNs0iHY5B74UhQF0dVMSNl9yjXoV1Bow74/LuChvi28HqN969r4bs7DHt/94+31qF87AiMeahPQAUAACDaICDa4w6AoCqJDJaQctqBJnJreBWYZ+45b8VCnG+Ox7KtxPfP55Zjtl2Y4e34vCO7ZRP6gkwQ0jVGw/RTQs/gdS7Ki/v1wS+/7JNcEtp8ChrRyf5dyEmhZo+y2IcUdquM5Cg6kA8+1V7+3FA9UlK6GJcF9IeFvwUYJwcZS6R+mQcrBfDSpq9YzBWYn9h0rxEM9KuemUYhJAiDh+HkL9qcV4bkB/l/X1mc+P+Yom899zMRz53PHNeXz5rEa6DRAWqYTbYpnENidCs5ky4gN91+9p5MENKspIiXNiZ6J6u/KioLtx5145Bbv3ZLkOBEpx50Y2s59MbMtzYnkOHc4L11AnLgoY/6jRkSUWvOteU0ROglIy5LRpvjtiXangjO5MmLDKmcGxfVK32axGmglIOWYwzWL81iGE2dz5TKDG4IgoHqoei5W/W5DzTABTWP9+/bIYL2I4BLRUxQF0SEiUv6yoUnspfMgY98pOx5q7z3P6jQCmtXSIOUvG3oWP+orywq2/2XHIx3VGwFPdjXhgbae+/f9MBuv9glC9ybuAPx5wYFhs3LQr7UBL/aqnAFANZ4VTO8zzvLTu2Zxejcpkd5pDjxS3Na50jvNgV7FeeJYphNncxVXen/0YBAsJVY0+P2sA69/Y8bnTwSjTvGgb+s6Gqw9aEehVUHQpfYgS4YowOPxwopS86qIlDQHejbRusN+zIFHbvV+UzK5toSUNCeGdnB/t+2oE8lx6nmMixDUc5jmcN3wU8uMEw/doh7z9bsNeL67u3FKz1cw4vMivD/QiJbF6/aa7WXbNLE4ipXVjvtOf7tnPj/twEM+6mb1HGqQcrRUv+WoHY+0L9k/lzF8Xj50Gnjtn3/0cAgsDnfEfj/twOvLC/H5k6GoU61yynug0vtqXUpnWyUsgerRbjUujruiYHuaE4/4eENtcpyElDQHhrZ3x2PbMYc77uGX4u50DXgWWIvri1s8j6koCpan2nFfkqbM5IyvnjCh5PthNh52YNZWG758woTqlfzkSqDab28OnVfrrsgbZF36vyNR4JqKN6ur6vX+/vvvqFu3bpnv69ati99//x2A+sjwuXM+VrK/AehNQYiu636TcVRcPOKaJKEwJxvZ506Vs2fgCYKAIR0NmLnZgvhICbUiRHy0wYyYENHVgQSAYXPz0LOpDo8WdyKG3WbAq8sK0TxWg6Q4DeZvs8BsA/q3KR5IsigYPi8PZjvwzsNBKLAqrjcIVgsSIIkCjhQPAHZK0GLYbQbX+ieSCFQLCuxgGVB8bgYkY+bnOxFfKxy1aobio8+2IyYqCD07uR8vHDZuBXp2qo9H72+JYJMOjepFehzHaNAiPNRQ5vsbgSAIGNItFDPX5CI+WotakRp8tCobMWEa9GzpHpQd9tF59GxpwqNd1GkXhVYZJ0uso3Q6y4E/TlsRZpIQW02tCnIKnTiX7UB6rjqdIe2C2nOKCpUQHXp9HzPwVz4H1MeEMgtknLyoxvPIBSeC9DJqhokIN4loVVuDUKOAV5epj7vrtQKW7rTiTLaMron+ffR9WCvgtXVA8+oKWlQHFqQCZgdwf1P131/9UUFMEDD2NrXT81gyMHQZ8NluBV3igdVHgP3pwKQe7mOu+VNBNaO6NuCRTGDyz0CP+sBtddVj1IsA6oQBEzcCL3dSEF78OPC2k8Anff0aXZ8EQcCQXjGY+e0FxFc3oFa0Dh8tO4eYcC16tnY/Wjhsyp/o2SYcj96hDvoXWpw4ecG95uzpDBv+OFGEsCANYqPUfLPm12xEhGgQG6nDkVNm/HvRGfRoE4ZOLfz/KFdpgiBgSIfifF5NRK0IqUQ+d+e1YZ/lo2cTrTufdzTg1eWFaF5LQlItDeanFOfz1u6y4crnxev0lM7nwQYBg9vqMX2jGTXCRMSGiZi7VV0z7q7mfs7n7bR49TsrmtcUkVRLwvwddpjtCvq3VH93/LdWxIQIGNddjc9jbbUYstCCudvt6JogYdUBBw6clfFW8eNCdqeC55dZcfCcjJmD9XAqimu9pTCjAJ0kIFgvYHAbDab/bEeNUAGx4SLmpqh14l1N/FO/+St9QwwCBrTWYeoPZoQZ1bj936oiJNeWPAYB5/xiQacELUQBWHfQhllbLPhgUJDHjde/0p2wOxXkFikotCn445xa7/uauXTV5+E2I2ZuLEJ8lIRa1SR89GMhYkJF9GzqrpuHzcpBz2Z6PFo8yDeskxGvLs1H8zgtkmprMP8XM8w2Bf3bqOdJnfVTtu8RGy4hrnjg48h5dQCwUyMdhnU2ufstAlDtCmbOVjie7fWY+bPV3W5tLE7vxiXSe16Bmt7t1LgP66jHqyuK0LyWRi0PKVY1vVuVSO9WOkxdUyK9V5s90rv0QE92kRrPBlGia/ZgnxY6/PcnC/7xTRGe7WZAdpGCt380Y0Ar3VU/Cjysgx6vrjCjeaykhn27TS3LxbOPxi83IyZUwLieapo91k6HIfOKMHebFV0barBqvx0Hzjrx1r2GEudQp57DamLxOVTrg56N1biWvklh0qlxrRMhokaY+m9dG2owP8WGGZutuKeFBoVW4IMNFsSGCWjq42mCa+XK55vM7vRfX1S23zInV+23dCjO57cZ8OqyArW8u/otSon+uToAaLYpeGdgiNf+eZ1Sszizi2deNYiWyqwleS0Ckd4AcDZHRq5ZwblcdU26P86p/bc61UQE6QX8dMSOzEIFLWIlmHQC/spw4p0frWhdW0JcJc18HtZBh1e/sahxjxXd7VbxSzrGf6OW9XE99O64zy/C3BQbujaUsGp/cbvVp0Tc22kxc0tx3MMFfLTZVibuALA9zYnTOQoGti7bNjeI9kz7/WfVwbFGMf7J54Fov/ecdmLfGRnt4kUE6QSknpYxeZ0V9zbXIMwP6/kSVXVX1cNr3LgxpkyZgk8//RQ6nVrA7XY7pkyZgsaNGwMAzpw5g+rVb9y1y+o2b4Nxn69z/T3oH+8AALYtX4D5rz4ZqGBV2IjOBphtCt78thB5FgVt6mgwa6jnncGTF2Vkl1h0tXcLPS4WKpi+wYyM4kcqZw0NcU1jP3DWgb3Fb6O684Ncj99bPy4McRES1u634WKhgu/22vDdXpvr32PDRWx8KdyPMa64EYNbw2yx4833NyGvwIo2LWpi1pS+Huv9nTybi+xcSwBDeW1G9AyF2SrjzS8zkWeW0aaBAbOeru7xxtOTmXZkF7gXK95/woqhH11w/T1lufqmiX7tgjDlMXUAZePvRfjH5+71B8d+lgEAGHN3GJ69p2JrLlUmf+RzAPjqVwtmbHKn/6Oz1UeK/tM/CP1b6xERJGLW0BB8uK4IQ+fmwyErSIiRMOORYDSuxAtjb+5uJOCiWcH07erLPRpHA/+7D4gqvtN5Lt9zdkOrmgLe7qXgoxTgw21A3XBgeh/3Y72A+gKQt7eojwlHBwH3NQZG3eo+hlYSMPM+BR9sBcasBIrsQJ1wYPIdQJf4wHWuRtwTo+bzz04ir8iJNg2DMOulBtDrSuTzdBuy8923+fenFWHo5L9cf0/54gwAoF+napjylHrzKj3HjilfnEFWrgPR4Rrcd1s1jO7nZerkdTKisx5mu4I3vyty5/MhwWXzeZH7jm3vFjpcLJQxfYPFnc+HBHvm89+snvl8jrp21X/uN6F/a7Xz/XIvIyQRGP91ISwOBS3jNJj3uH9fgAMAvZtpcLFIwfSf7MgotKFJdRGzHjK4Hic6mytDENxhaF1bwrv99Phwsw0fbLIhvpqAjwfp0ShG3eZCvoKNR9T6rt8sz7p9/qMGtItXL4Ze7qGDJNow/jsrLHagZS0R8x41+vUiwl/p+9rdJoiCGc9/VQibQ0GnBC3evNdzdv7PR+yY+ZMFNoeCxjUkzHg4GLc38ryIfGphAc6WePz7/k/U+vDQvyq3zh/RxajW58vz1fMQr8Wsx8M8z0OW07M+b2lQ6/N1hcjIl9EkVoNZT4R5PPJ8OWt/t6r9lj1WfLfHfYMgNlzExlcr/0bfiE7F6b2yRHo/GuQZz2wnsovcF+i9m+vUeG40I6NAfQxy1mNBnul9lxGiADy/uMid3vdc2dIIQXoBc4cE4/9Wm/HAp/kINwq4q5kOL/S4+pcn9G6uVcO+yVocdhGzHjW5wl6mLNfR4N0BRny40YoPNqiDHx8PNqJRdff5GHGbTs0rKy3F51DCrEdNXp9O8aV9ffV35my1Ys5WKwxaAcm1Jcx+1OTXtQ9d/ZZvCtSw19Vi1rDQ8st7kr64vJvVfF5Tg1nDSvbPndhb/BboO9/P8fi99S+FIy7CvzN7SwpUen+0yYpv9rpvZN//P3UN6flDTWhXT6PerN1lw5Q1Ftic6szWO5to8GSnylsjsXez4rhvLo57dRGzHi4ZdwWC4K6/WteW8G5/Az7cZMUHGxU17g8aPQbnRnTUwWxT1/Vzxf0RU5k3u3+dakerOBH1o65fWvsSiPZbJwGrDzrw8c8ybE51FuXQdlo83s6/NyyJqipBUa78Ya9t27ahb9++EEURSUlJANTZgU6nE99//z3at2+PhQsX4vz583j55ZcrdMyRjW7OhWxn/rt1oIMQGB0fCXQIAuPgykCHIDBy8gIdgoCQ03280reKE29JDnQQAuPE8UCHIDCststvUxXp/75rsF4T7d9/8fmrYr3+bw+/ISj+Wx/0hqa9SQcX7DdpPvfnWi83MvnmLN/CY7sDHYQbwktNb9J+TAnvHrw5+7BX1ZPr2LEj0tLSsGjRIhw5cgQAMHDgQDz88MMICVHXJHvssccqL5RERERERERERER01a76dm5ISAhuv/12xMfHw2ZTR1A3bdoEAOjbN0CLSBEREREREREREVEZVzUIeOzYMdx///34/fffIQgCFEWBUOI1mk6ns5y9iYiIiIiIiIgoEPhKlZvXVa38/fzzz6NevXpIT0+HyWTC/v378dNPP+GWW27B5s2bKzmIREREREREREREdC2uaiZgSkoKNm7ciKioKIiiCEmS0KlTJ0yePBnPPfcc9uzZU9nhJCIiIiIiIiIioqt0VTMBnU6n6wUgUVFROHv2LACgbt26OHz4cOWFjoiIiIiIiIiIiK7ZVc0EbN68Ofbu3Yt69eqhXbt2ePvtt6HT6fDpp5+ifv36lR1GIiIiIiIiIiIiugZXNQj4xhtvoLCwEADw1ltvoU+fPujcuTMiIyOxePHiSg0gERERERERERFVDpFvBrlpXdUgYK9evVz/n5CQgEOHDuHixYuIiIjweEswERERERERERERBd5VDQJ6U61atco6FBEREREREREREVWiq3oxCBEREREREREREf19cBCQiIiIiIiIiIioiqu0x4GJiIiIiIiIiOjGxtlgNy+mPRERERERERERURXHQUAiIiIiIiIiIqIqjoOAREREREREREREVRwHAYmIiIiIiIiIiKo4vhiEiIiIiIiIiOgmIQiBDgEFCmcCEhERERERERERVXEcBCQiIiIiIiIiIqriOAhIRERERERERERUxXEQkIiIiIiIiIiIqIrji0GIiIiIiIiIiG4SnA1282LaExERERERERERVXEcBCQiIiIiIiIiIqriOAhIRERERERERERUxXEQkIiIiIiIiIiIqIrji0GIiIiIiIiIiG4SghDoEFCgcCYgERERERERERFRFcdBQCIiIiIiIiIioiqOg4BERERERERERERVHAcBiYiIiIiIiIiIqji+GISIiIiIiIiI6CbB2WA3rxtmEHDmv1sHOggBMer13YEOQkDMfCU/0EEICEtWYaCDEBCGujGBDkJAiGFBgQ5CYBTdnOUbGinQIQiMkGqBDkFgZF4MdAgCIzQk0CEIDIs10CEIDFkJdAiIyF+czkCHgIgCgAPAREREREREREREVRwHAYmIiIiIiIiIiKo4DgISERERERERERFVcTfMmoBERERERERERORfohDoEFCgcCYgERERERERERFRFcdBQCIiIiIiIiIioiqOg4BERERERERERERVHAcBiYiIiIiIiIiIqji+GISIiIiIiIiI6CbB94LcvDgTkIiIiIiIiIiIqIrjICAREREREREREVEVx0FAIiIiIiIiIiKiKo6DgERERERERERERFUcBwGJiIiIiIiIiIiqOL4dmIiIiIiIiIjoJsHZYDcvpj0REREREREREVEVx0FAIiIiIiIiIiKiKo6DgERERERERERERFUcBwGJiIiIiIiIiIiqOL4YhIiIiIiIiIjoJiEIgQ4BBQpnAhIREREREREREVVxHAQkIiIiIiIiIiKq4jgISEREREREREREVMVxEJCIiIiIiIiIiKiK44tBiIiIiIiIiIhuEpwNdvNi2hMREREREREREVVxHAQkIiIiIiIiIiKq4jgISEREREREREREVMVxEJCIiIiIiIiIiKiKu6oXgzidTqxYsQJ//PEHAKBJkybo168fNBq+Z4SIiIiIiIiI6EYlCoEOAQXKFY/aHThwAH379sX58+eRmJgIAJg6dSqio6OxcuVKNG/evNIDSURERERERERERFfvih8HHjFiBJo1a4bTp09j9+7d2L17N06dOoWkpCQ89dRT/ggjERERERERERERXYMrngmYmpqKnTt3IiIiwvVdREQE/v3vf6Nt27aVGriKUBQF0zeYsXSnFXkWBa3raDChbxDio6Ry91u03YI5v1iQWSCjcQ0Jb/QJQlKcejpyimRM32jG1r/sOJcjo1qQiB5NtHi+pxEhBnXc9NA5Bz792YLdJxzILpJRK0LE4LYGDOlo8Hucr0XDWzrhzhFjUadZK4RXj8UnTw/E3vXfBTpYFbZotxNzfpORWQg0jhHwRg8RSTV9j2WvOSxj2i9OnMkF6kYAL3WR0KW+5/ZHsxS8+5MTv51S4FSABpECPrpPQmyogByzgulbZWw9LuNcPlDNCPRoKOL5TiJC9IGbQy21egyadk9BCIqGkv4HbOsnQjm31+u2QlRDaDu9CKFGC4hhcbBteAvOnZ95bKMftQViWFyZfR27F8K+7k2/xMEbRVEwfZMFS3fZ3OW5jxHxkZcpzzusmLPNgswCBY2rS3ijt9FVngHAalcwda0Zq/bbYXcquK2BFhP6GBEV7JkXlu+xYl6KFcezZATrBdzVVIs3+5hc//7Dfhv+t8WC41kyqpkEPHKrHsM7VX6ZX/SbDXNS7MXxEfHGXXok1fJ9DtYcdGDaZivO5CioW03ESz106NLQHf8f/3Dgq912HDjnRK4ZWPGkEU1qeB4vo0DGO+tt2HbMiUKbgnqRIkZ20qFXk8Au86AoCqavvIilv+QhzyyjdQMDJjwUjfjqOp/7/PanGXN+zMaBk1Zk5Drx8aga6Jkc7LHN9JVZWL2zAOezHdBqBDSro8cL90WiZb3rX4crioLp64uw9DcL8swKWtfVYkK/4Mu3YylmzPnZXNyOafBG3yAk1da6/v3NFQVI+cuG9DwZJr2AVnW0eOkuE+rHqGmaXSjj5cX5OHzeiZwiGZHBIro30WFsLxOCDf5fMlhRFEz/IQ9Ltxeo8a6nw4SBEYiP1pa736It+ZizMR+Z+U40jtXhjQHhSKqrd/17Rp4T73yXg22HLSi0KqgXo8HIO0LRq6Valk9nOfDfH/Ow/U8LMvNlxISKuPeWIIy6IxQ6jf/r9UU77Z7lu5fu8uX7J1tx+RbU8p2gpqHdqWDaZjt++suB0zkKgvUCOtaTMLa7FtVD3Gk4erEFhy7IyCpUEGYU0CFexLgeOo9t/E1N71wsTSmZ3tUQH1OR9M5DZp4TjWvp8MaACI/0Pplpx9vf5GDXMStsDgWdmxjxxoAIRIW6z+noWRk4dNqGrAInwkwiOjQyYFzfcFQPq/z6bdGvNszZalXTt4aIN+42IimunPQ9YMe0jVacyZFRN1LESz316NLIfU7UdtGKpbvtartYW8KEPgav7aLNoWDQrEIcuiBjxcggNKmpbjN9kwUzfrKV2d6oBfa8Hnrtcf7NhjnbbO48fbfhMnnajmmbbO4499B7tFmKomD6ZhuW7ikR594GxEd65tfNRxz45GcrDqfL0GuAtnU1mPGg0fXvv59x4r0NVhw454QgAC1iJbzcU4/GNcqvW6+F67rkN4sa9rrail+XbClRn/cxuerznCIZ0zeYsfUvm/u6pKnO47qkpOwiGf2m5+JCnoxf34hAqPHayvmV5EGPOF2mLFjtCqb+aMGq/Q7YHQpuS9Bgwj0Gj77Z2RwZk1ZZsCPNAZNOQL9kLcb20EMjCR6/s+hXNT/VDBMxqrMO/ZLdfYQ/0534aJMVB846cTZXwWu99BjawV2HXIvKzvs//mHHV7tK9NeeMpXpry3eZcP3+x04eM6JQhvw6yvBCDUE9rnORbscmLPDgcyC4uuzO7VIii3n+uwPJ6b97MCZ3OJ2rasGXRK8n7cJa+xYvMeJ13poMPRWLj1GVBmuuFVo1KgRLly4UOb79PR0JCQkVEqgrsTsLRYs3G7FxPuCsGRUKIw6ASPm58NqV3zus/p3K6b8UIQx3YxY/nQYEmtoMGJePrIKZABAer6M9DwZr9xlwspnwzC5fxC2/GnH6ysKXcc4cNaJyGABbw8MwvfPhWFUFyPeX1eEz7db/B7na6EzBeH0oX348q3nAx2UK7b6kIwpm2WM6Shh+RANEqOBEUudyCr0nta7z8gYt9KJB1qIWDFUg54NRTyzwokjGe7tT2YrePgLB+pXE7BgsAbfDtXg6Q4i9MXtUHoBkF6g4JWuElYO02Dy3RK2pMl4fY3zekTZK6nxPdB2fx2OrdNgndcHcvof0A+aD5give+gMULOOQXHT1OhFKR73cQ6/z6YP27r+li/ehQA4Dy0yl/R8Gr2L1Ys3GHFxHtNWPJkCIxaYMTCwvLL834bpqw1Y0xXA5aPDEFiDQkjFha6yjMATF5jxqYjdkwbZMKCx4ORni/j2a8KPY7z2TYLPtxgwZOdDPh+TAg+GxKMTgnuC7Gf/7Tj5WVFGHyLHiufDsGbfUyYt92Kz3dYK/UcrD5gx5R1Noy5XYflT5qQWF3EiC/MyCqUvW6/+5QT45Zb8ECyFiueNKFnooRnllhwJN2dR812BW1qS3iph+9O7/hvrUjLkvHJgwZ8N9KEOxpr8OIyCw6eC1xeB4DZP+Zg4aZcTHw4GkvGx8GoEzFi+llY7d7PBwCYrTIax+nx5uBon9vEV9fhn4Oj8d0/62DRS7VQK1KL4dPO4mL+9Y/v7J/NWLjNgon9grHk6XC1HZubW36+32fFlFWFGNPDhOXPhCOxpoQRc/M88n2zWhr854EQrBobgdmPh0EBMHxuHpyyelxRBHo01eGTISFYMy4Ckx8IRspROyZ8U+jjVyvX7A35WPhzPiYOrIYlL8ao8Z6ZUX68dxdhyjc5GHNXKJa/VAOJtbQYMTMDWSXSbfyiLKSlO/DJiCh890oN3JFkxIvzsnDwtDoIkpZuh6womDSoGr4fXwOv3R+BxVsL8MGqXL/HefUBh1q+O2uxfIRRLd9fWny3Y6ecGLfCigeSNVjxpBE9EzV4ZokVR9LVdLbYgYPnnXi6sw7LRhgx/QE90rJkPL3Es15qFy/hg/56/DDaiGkD9DiZo+D5ZZVbd12OK70HVcOSF6urZXlm+mXSuxBTVmRjTK8wLH+5JhJjtRjx33RXehdZZQz/JAOCAMx7JgZfvFAddqeC0bMyIMvu47ZL0OODx6Pww+uxmPZENE5mOvD83MxKj+Pq/XZMWWvBmK56LB8ZhMTqEkZ87tkelbT7pAPjvjbjgdZarBgVhJ6NNXjmKzOOXHDn59lbbVi4w4aJfQxYMiJILScLi7yet3fWWRHjZWD3iY56bBkX7PFJiBbRq2n5A7AVivMBO6b8aMWYLnosf8qktsGLispvs5ZZ8EArLVY8ZVLz9GKzR5s1e5sNC3+1YeI9eiwZboJRK2DEoiJYHe44r/3DjvHfmNE/WYtvRgbhi8dN6NPcPUBQaFMw4osi1AwTsHh4EBYNMyFIL2DEIjPsTt957lrN3mLBwhQLJt4XjCWjw9R+zLy8y9fnqwsxprsRy8eEqeew9HVJvoxX7grCyufCMXlAMLYcseH15d7r6jeWFyCxEgc6ryQPuuJUgbIwea0Fmw47MG2gEQseD0J6voJnF5td/+6UFYz8ogh2p4Ivhwdhyv0GrEi146NN7rrry99seH+9Bc901eP7p4PxbFc93lptwcbDdtc2FjtQO0LEuJ4GRAdX3mCZP/K+2Y7L9tcsdqBzAwkjO/m+GXo9rT7oxJQNDozppMHyJ3Rqu7bY5rtdOy1j3Ld2PNBSwoondOr12TI7jmSUPW/rDjux94yMmGAvByKiq1ahQcC8vDzXZ/LkyXjuuefw9ddf4/Tp0zh9+jS+/vprvPDCC5g6daq/w+tBURQs2GbBqK4G9GiiQ2INDaY+EIT0fBnr/yh7x/OSeVstGHiLHgPa6JEQI2FSXxMMWmDZLrVRaVRdg+kPh6B7Yx3qREpo30CLF+8wYdMhOxzFHYcBbfR4/Z4g3FpPi9rVJPRN1qN/az3WHfD9uzeCAz+vxbcfTkTqur/P7L9L5u2UMTBJxIAWIhKiBEy6U1LTbb/3xnbhLhmd6gkYfquEBpECnu8koWl1AYv2uLf/8BcnutQX8HJX9d/qRAjoniAiMkjtJDSKFjC9nwbdE0TUiRDQvq6IFztL2HRUgUP2XyeyPJq2I+DcuxjO37+GkvUX7GtfB+xmaFoM9Lq9cn4fHJsnw/nH91CcPvKn+SJQmOn6SAndIWcfh3xqhx9jUiqcioIF260YdbsBPRprkVhDwtT+xeX5kN3nfvO2WTGwjQ4DWhWX5z5GNV/sUeOab1GwbI8N43sZ0b6+Fs1jNZjcz4Q9p5xIPeUAAOSaZUzbaMHU/ibcm6RDnWoSEmtI6N7YfYH07V4bejTWYnBbPWpXk9C1kRZPdTJg9i8WKErl5YV52+0Y2EqLAclaJESLmHSPHgatgGWpDq/bL/zVjk4JEoZ31KFBtIjnu+nRtKaIRb+5z9l9SVqMuV2HDvV8XxSknnLi0bZaJNWSUDtCxOjOOoQYgAPnfQ+2+ZuiKFiwIQej7o5Aj+RgJMbpMfXxGKTnOLE+1fdA1e3Ng/DCfZG4o5XvXuO9t4agYxMTakdr0TBWj1cfiEKBRcbhM9d3YERRFCzYasaobkb0aKpHYk0Npg5SB6rXHyynHdtixsC2Bgy4xYCE6hpM6hcMg07Asp3uG1EP3mpA23paxEVIaFZLgxfuMOFcrowz2WqahhlFPNTeiBZxWtSKkNAhQYeH2hmw67jv8lap8f45H6PuDEWPFkYkxuow9ZFIpOc6sf53s8/95m3Ox8AOwRjQLhgJNbSYNDACBp2IZTvc+SE1zYZHOwcjqa4etaM0GH1nGEKMIg6cUs9n5yZGTH44Ep0aG1A7SoPuzY14onsI1u0r8nu85+2wY2Arjbt899YVl2/v53zhb3Z0aiBheAcdGkSJeL6rTi3fO9XtQwwC5j5ixN1NNagfKSI5TsI/79LhwDkZZ3PdZXdYOy2S4yTUChfRuraEpzpqsfe07NfBkJIURcGCn/Iw6s4w9GhhQmItHaY+eim9fZ/3eZvzMbBjMAa0L07vQdXU9N5eAADYnWbFmYsOTH4kEomxOiTG6jDlkUjsP2XD9j/dZXlYt1Akx+tRq5oGrevp8VTPUOw9Yav0+M9LsWJgay0GtNIVt0cGNX33+EjfHTZ0StBg+G16NIiW8Hx3A5rWlLDoVzWvqu2iDaNu17vbxfuNSM9XsP6QZ5vw8592bD3qwCt3lh08CNILiA4RXZ+sQgV/Zch4oPW1DwLOS7GpcU7WIiFacrdZ5cb5UpsllWmzFEXBgh12jOqsR49ELRKrS5jaz+ARZ4es4D9rrHj5Dj0G36JDvUgRCdES7m7mjs+xTBm5ZuC5rnrUjxLRMEbCmNt1yCxUcDbXP/neVZ93NaJH0+LrkoHBV3BdYkBCjAaT7gvyfl3SpPR1ic11XXLJlzvUGYhPdDJ6+6mri1MF86BHnC5TFvItCpbttmN8LwPa19egeayEyfcZPPpmW486cDRDxtv9jWhSU8LtDbV4vpseX/xmg614QPjbvXY8eIsOvZtrUbuaiHtaaDGojQ6zf3Gf7xa1JLxypwH3tNBCW4mTQCs77wPF/bUuenSo73vG29D2OjzVSY+W5cwwvp7m/erAwJYSBiRpkBAlYtJdGhg0wLJ93m+qLtzpQKf6Ioa316jtWhctmtYQsGiX5/YX8hX83zo73umrhebGiGqVI/Bz06rQIGB4eDgiIiIQERGBe++9FwcPHsSgQYNQt25d1K1bF4MGDcL+/ftx7733+ju8Hk5ny8goUNCxgbvRDzGISIrTuBqQ0mwOBQfOOj32EUUBHRpofe4DAPkW9fHAktPPy26jIMx0M2cn/7E5FRw4r6BjXff5FQUBHeoKSD3rvTOXetZzewC4LV5A6ln1wkhWFGw+qiA+QsDwpQ50nGHHoM8dWP9n+YMe+VYgWAdoAvFKJVELoUZzOE/8UuJLBc7jWyHWal1pvyE17QfnvqWVc7wKcpXnEh2fEIOApFpS+eX5nNNjH1EU0KG+uw44cNYBuxMe29SPlhAbJri22XbUAVkBLuQp6D09D13ey8ULSwpxrsRFtM0B6Ev1yQxa4HyegjM5lTNQZnMqOHBORscSg3WiIKBDPQmpp713plJPOz22B4Db6ktIPX1lYUquLWH1QQdyzApkRcGq/XbYHMCtdQPX8zqd6UBGnhMdm7gfyQ4xSkiqp0fqscqbdW1zKFi8JRchRhGN4yrnEaGKOp0tIyNfQccE9x39EIOIpNoapJ70fiGhtmMOdEzw0o6d9F5WimwKlu+yIC5CRI0w703/hTwn1h2wom29ax8YuJzTWU5k5Mno2Mj9+HWIUURSXT1Sj3sfiLU5FBw4bUPHRu40EkUBHRp57pNcT4fVe4qQU+iELCtYtbsINoeCWxN8p22+WUGYyb+Pxvos3/ESUs94L6+pp+UrLt/5FrVj6+vxsByzgpX7HWgVJ0JbTp+mMpWb3mnlpPcpm8c+anobkHrc5tpGEODxGLdeK0AUgF0+6oicQidW7ipCq3h9pcZfLZey9/bIV/19yomO9Uulb4K7vj+drXhvF+M824TMAhn//M6CqfcbYdBePk5Ld9sQHynilrrX9mhd+W2WrzztRMd6nr97WwP3OTqdcynO7mO6+gLF2xw8J+NCvgJBEHD/p4Xo/H4BnlxU5DGjql6kiHCjgK/32GFzKrDYFSxLtaNBlIha4f7J9+Vel/iom931ubsNEEUBHRJ0PtsAQL3mKH1d8le6A59sLMLUB4IhVFIUK5oHS6pIWThw1gm77KNvVrxN6iknGsWIHo8Hd0rQoMAK/FU8a8zmVMr2zTTqo+D+vMnhj7z/d+S6PqvnTiO1XRN9t2tnZHSM92xvb6vnub2sKHhlpR3D22nQMPr6LVtBdLOoUOu/adOmSv1Rq9UKq9Wz06ezy9Brr6yQZxRPKY8sta5XVLCIzHzvFX92kQKnDESWmg4eFSwiLdN7Y5tdKOO/mywY1Nb3BcTuk3b88LsNMx8LuZIoUAVlmwGnAkSaPL+PMglIu+g9rTML4ZrR59o+SP0eALIKgSI7MOtXGc93EvHS7RK2HJfx7DdOzB8M3Frb2zorCv6b4sSglgFqkEwREESNOmOvBKUoE2Jkg0r5CanRnYAhFI79X1fK8Soqo0BNR6/lueBy5bnsPmmZDtdxtRLKrIkTWeK4p7JlKArwvy0W/ONuI0L0AqZttOCJBQX4dnQIdBoBnRI0mLLGjJRjdrSL1+DERRmfbbO6fiMuAtcsu0hdl7JM/RQkIC3Te2cqs0Apm8+DRWQW+r6p4c2HAwx4cZkF7d8thEZUBzinDzSgbrXAdb4y8tQ4RIZ6XiRHhWiQmXftneZN+woxbs55mG0KokMlzH0+FhHB13fQMyO/vHbMe5pnF8ne832IiLQMz3bsixQz3l1TiCIbUC9awtzhYWXWvRv7ZR42/mGDxQ50a6LD//X3/3M3GcWPc0aGlE5b0WfaZhcWx7vMPhLSLrjz+4dDo/Di/Cy0f/2smpd1AqY/EYW6PtYaPJFhx+db8vHKfeHXEKPLc5XvMuVVQFrWFZTvIAGZPh43szoUvLvRhnuaSQgutW7tuxtsWLTTDrMdaFlLxMwHr9/6l77TW/KdzwudPtJbRFq6ms+T4/Uw6gS8+10OXuwTBkUB3luZA6cMZOR5Hvfd77KxaEsBzDYFLeN1mPmU7+UCrkb59bf3PJ1ZoJQtx0Hutsndz/WSB4r/TVEUvPaNGYNv0aFFLQmns8u/AWS1K/h+nx1Pdrr2Gx7uPF06DpeLs7f4XIpzcV/ASzkp2WYDwIyfrBh/px61wkR8tt2GIfPNWPNMEMKNAoL1AhYMNeKZxWb8d4s6aFy3mojZjxj9dhO33PrcxyPhvq9LBKRl+Oj7FMr472YzBrV1l2GbQ8G4xQV4+e4gxIZLOHWxcm5OViQPlglfBcqCu2/muU1kqbxQ+hiX8oX62xI6NdDg69129GisRbOaIvaflfH1bjvsshqOmBD/pLU/8v7fUXbRpeszL2nts13zdn3meR5mpTghCcBjt3AKIJE/VGgQsEuXLpX6o5MnT8akSZM8vnvzgVqYOKh2ufutTLViwnfuR36ux4BbgUXByIX5aBAj4Znu3qfWH7ngwJjPCzCmmxGdGvp/BgVVjktNU/cEAcOKG5km1SXsOaPgq1S5zCBggVXByOVONIgU8EzHqntXSkoaBPnYT4CP9QMry8p9NkxY6X4MbOYjgVvwQ1YAuxN4/W6jax3A9x4wodM7edhx3IHOCerjJacuyhi1qBAOGQjWC3isnR4fb7YgEJNCK9u0zTbkWxR89qgBEUYB6w878eIyCz4fakRi9evTCVu5Ix8TvnDnu5ljYv36e+0SjVjxem1kF8hY+ksuXph1HkvGxyEy1H8LT6/cY8GEbwpcf88cGua33wKAe1vp0bGhDhn5MuZuMeOFL/Lw5ahw6EvMFnqtTzCe6SHjeKYT768twpRVhZjQr3LL48qdhZiwJNv198ynoir1+CVN+yEX+WYZnz0djYggEet/N+PFeZn4/LkYJMZ6rqF0IceBJ/+XibuSTRjU4e+96JDdqeCF4nX+JvYuO8AzvIMWA5I1OJurYMbPNrz6nRUzH9RDqKwpQyWs3FmICYsvuv6eObJyB9wuqRYs4cPHozBpSTYW/pwPUQDuaW1C0zhtmXp5ePdQDGgfjLMXHZixJg+vfp6FmU9F+yX+19PCHTYUWoGnOldsfbB1hxwotAH9kv++/dVLq7GoL69S4zG5rwFdPizEmoN2DG6jg8Wu4I3vLGhVW8J7/XVwKsDcFBtGfWnG0hGmCs2YvJyVqVZM+LZEfT7k2l+ycjkFFhkjF+SjQbSEZ3q4r0ve+7EIDaLV5Ymuxcp9dkxY6V6SYeYjpnK2Drynu+iRWaBg8OxCKMUDj/2StZi91VYl+mY3o/3nZCzc6cCyx/3TPhHRVbwdGABycnIwZ84c/PHHHwCAZs2a4YknnkBYWMUuZl577TWMHTvW4zvd93dcdr9uTXRIqu0O8qX1ILIKZI+FkDMLZNcb0UqLMAmQRCCr1F2XzAK5zJtCC6wKRszPR5BOwMcPB3t9bOSvdCcen5uPQW31GN2tctbfoLIijIAkAFmllg7KLFIQFeR9n6gglFmUNrMQru0jjIBGBBIiPdO1QaSAXac99yuwKRjxtRNBWuDjftJ1e4SqjKJsKLIDCPK8gBZMUVAKM6758EJoLYh1b4NtxehrPtbldEvUIqmWeyDfVnzj1Gt59rHAtbs8e95tVMuzmkbRwQLsTiDPLHvMBswquU3xneKEaPfvVAsSEWEScK74UV9BEPDSnUa82NOAzAIFESYB29PU2Ue1IypnUDjCJKj5vHT9VKi4wlpaVLBQNp8XyIgKqngePXlRxqLf7Fg50oiGMeo5aFxDwq5TTnyx045J91yfQcBuLYOQVM99M8hVx+c5EVPiLZ6Z+Q40qYTHdk16EXVjdKgbAyTXN6DXP0/g6215GHlXtWs+ti/dmuqQVNs9bdTmLNGOhZZux7w30REm0Xu+z5cRVeqlACEGESEGID5KQsvaGrR7KwvrDtjQp8SF4qV1wurHaBBmEvHI/3IxurvJIzzXqltzI5LqugcpbMUT97LynYgJc+evzHwZTWp5H5yICCqOd6mXt2TmOxFVHNaTmQ4s2lKAleNroGFN9TiNa+mw65gVX/xSgEmD3Gl7IdeJITMy0Cpeh7cGVcJU3stwle8y5fUKy3ehgqhSs0/sTgUvLrfibK6CeY8ayswCvPT7ESYB9SKBBlF6dP3IjNQzMlr5YV0pNb1ruP52leUy6e0sJ70lH+ktI6rE7MBOjY1Y96YR2QVOSKKAUJOITm+cRu1Iz/ITESwhIlhCvRgtGtTQouuEs0g9bkOrepWzBED59bf3shQVLJQtx4Ul2y91v6wCBTEhJbdRXO3ijjQnUk87kfSvfI/jPPBpIfokaTH1fs++6de7bejaSOMzTFfCnafVWVklw1d+nH23cZde3JBVWCrOBQqa1BA9tkko8ZigTiOgdriAc8Xr/X2/344zuQq+Gm6AWDyQ8G5/A9q9XYANhx24p/m1D4Kq1yXhrr89rksqXJ/7ui4pWy+4rkv0Aj5+JMSjL7rjqB1HLjix9p9ZAIBLSxV3+E82RnYx4rmeFRvM65aoQVIt9w0RdxvlOw96jdNlyoK7b6Z4zAbMKpUXfj/jeYxL9eGl4xi0Av7Tz4hJ9xqQVaAgOkTAkl12BOmAan5coskfef/vKMJ06frsSvqt3q7P3NvvOiUjqxDoPsP91KBTAaZudGD+Tgc2Pn39ZrETVVVX3APYuXMnGjRogA8++AAXL17ExYsX8f7776NBgwbYvXt3hY6h1+sRGhrq8anIo8DBegF1IyXXJyFGQnSwgJSj7sefCiwK9p12ILm298ZWpxHQLFZCyjH3PrKsYPsxu8c+BRYFw+flQSsBnzwa4jFr4pI/LzgwdE4e+rXS48U7buw7ZX93OklAsxoCUk64Gw1ZUbD9hILkWO+NTHKsgJSTno3MthMKkotfWa+TBDSvUfZx4uMXFcSWGM8usCoYvsSp5oX+EvSaADbWsh3K+f2Q6t5W4ksBUnxHyGcqVv7KI7V4ACjKgnx04zUf63LKlOdoUS3Px9yP9RVYFOw74yy/PNeUPPaRZQXb09x1QLNYDbQSkJLm3uZYphNncxXXNq2L/5uW5b7YzCmSkV2kIDbcs26SRAHVQ0XoNAJW/W5Dcm0J1YIqZ7BEJwloVlNEynF3OGRFwfY0J5J9XKgnx0lISfO8SN6W5kRyXMXDZC5+w59Y6o6rKLhnXFwPwYZLg3LqJ6GmDtGhElIOuUf/C8wy9qVZkVy/8juBsqLAVs7bDitDsF5E3SjJ9UmIkRAdIiDlqHsR8wKLjH2nHEiu4/0iVW3HNB5tnywr2H7UjuQ65d/bU+C+qPPmUnqXt83VCDaIqButdX0SamgQHSoi5U/3um0FFhn7TliRHO99UEanEdAsToeUEi98kGUF24+49zHb1AGV0jNAREHwyMsXchwY8nE6msXp8J+Hq0G8DlNGXOU7rVT5Pu5Eci3v5TU5zrM+AMqW70sDgCcuyvjsEQMiKnDh60rnK1s1oMLKprdWTe8jXtLbxyCcTiOgWW2dxz5qeluQHF921ltEsIRQk4jtRyzIKpDRrbnvG7Ny8bibzVF5+Vwtl6JHW6P2Lx2+6+/aXurvo+76Pi5CUNvFtFLt4mn3Nq/fbcA3o4Kwovjzv+KZW+8PNOLF7p7n9nS2jB1pTgxoVTmzAH3m6XLaILXN8sx420qco7jwS3F2H7PAWtwXKN6meawEnQSPxw3tTgVnchXEhqn532xX64GSpeHS35XVrvm8LjlW8rpEVq9LfNTN5dfn7nQqsMgY/pnv65KPHg7BN8+GYcUz6udf96t3vD9/MhSPtK94e6nGSXR9XH2zcvKg9ziVXxaaxUrQij76ZsXbJNeWcCRd9hgo33rUgWC95wAwAGglATXCREiigFX77ejaSOPXet0fef/vyHV9dtxzPb/tJ2Tf7VotESknPG9+bDvu3r5vcwnfjtBhxXD3JyYYGN5OwuwHb4w3IlcVosDPzeqKZwK++OKL6Nu3L2bNmgWNRt3d4XBgxIgReOGFF/Dzzz9XeiB9EQQBQzoaMHOzBfGREmpFiPhogxkxISJ6NnFXEsPm5qFnUx0eLW4Eh91mwKvLCtE8VoOkOA3mb7PAbAP6t1E7S5cGAM124J2Hg1BgVVBgVXsM1YIESKKAIxccGDY3H50StBh2m8G1DogkotIGBPxBbwpCdF332nFRcfGIa5KEwpxsZJ87FcCQXd6wW0S8utqJ5jUEJNUUMH+nDLMd6N9cPd/jVzkQEyJg3O1qY/pYGxFDvnJi7m9OdK0vYtUhGQfOK3jrTndjO7ytiLErnbglTka7OgK2pMnYdFTBgsHqNgVWBcOXOmG2K3jnHnUxYldeMKkDQteb47fZ0N7zHuTz+yCf2wvNLU8AWhMcv6tr+GnveQ9K/nk4fn5H3UHUQohKAAAIohZCcA0IMU0AWxGUnBMljixAajEQjv3LAOX6L1IsCAKGtNdj5s9Wd3neWFyeS7yld9i8AvRsosWj7dTyOqyjHq+uKELzWhok1ZIwP8WqludWah0QYhAwoJUOU9eYEVa8TtD/rTYjubbkGgSsFyWhR2MN/vODGZPuVbd5f70Z9aNEtCtexDm7UMbag3bcGq+B1aFg+R4b1hywY+HjlfsI4bD2Wrz6rRXNa4pIipUw/1cbzHYF/Vuq4Rj/jUXN5z3U+D92qxZDFpgxN8WGrg01WHXAjgNnZbx1j/viL8es4FyujPTitVIvXThFBQuIDhZRP0pE3WoCJqy24JWeeoQbBaw/7MC2Y07MHBy4O66CIGBIj3DM/CEb8TE61IrS4KPvLiImXELPZPcU4GEfnEHP5CA82i0cAFBokXGyxNp4pzMd+OOUFWFBImKraVFklTHzh2x0TwpCdJiE7AInvvgpFxdynLirzfV9JFQQBAy5zYiZG81qvq8m4aN1RWq+b1qiHZudq7ZjHdWBjWGdjXh1ab6a72trMH+rBWabgv5t1PQ6ddGJ1fusuK2hDtWCBJzPlTHrJzP0GgFdEtXj/nTIhswCGS3iNDDpBfx1wYl3fihE67oaxEX496JEEAQMuT0EM3/MQ3y0Vo336jzEhEno2cI9eDNsRjp6JhnxaGd1CsqwriF49YssNK+tQ1IdHeb/lA+zTUb/dmp+qF9di7pRGkxYko1X7gtDeJCE9b8XYdsRC2Y+qc6gVgcAMxBbTcL4+8JwscQFZnSof+M9rJ0Wr35XXL5rSZi/w15cvtU6bvy3VrV8d1fT6LG2WgxZaMHc7XZ0TZCw6oBDLd/Fj/vanQqeX2bFwXMyZg7Ww6korjXVwowCdJKAvWec+P2sjDa1RYQaBJzKVjDtJxvqRAhodQU3C66FIAgY0iUUM3/MRXy0BrUiNfhodW5xertvog77+AJ6Jpnw6O0l0ntRFprX0SGpjr5EervL6bLtBWhQQ4tqwSJS02z49/JsDO0SgvrV1XO697gVv5+0oU19PUJNIk5lOjBtdQ7qRGkqbRagK/wd9Hh1hRnNYyU1fbcX19/Fg27jl5sREypgXE+1nD7WToch84owd5tVrb/323HgrBNv3Wtwn7f2OrVdrCYWt4tqHunZWG0TSt+oMunU/FzHy0uAlu2xITpEwO0NK2/Jg2EddHj1G4sa51jRnaeLHzce/43ajrvarHY6DJlfVNxmSVi1vzhP9ykR53ZazNxSHOdwAR9ttnnEOVgvYPAtWkzfbEONUBGxYSLmpqg3Uu5qqv7ubfUlvLNOwVs/WPFoWy1kBZi11QZJBNrF+6ecu+rzTWZ3P2Z9UdnrkjnF9XmH4vr8NgNeXVaA5rWkEtclSonrEhnD5+XDbFPwzsAQr9cldSI945RdPNOqQbRUZk3kK47TZfIgAAybX4iejbV4tJ0az8uVhRCDgAGttZi61lKib2ZBcpy7b3ZbAw0aRIt4ZYUZL99hQEaBgmkbrXi4rc61tm1aphO/n3EiKU5Cnll9K/Gf6TKm9HP3EWwOBUeLXyRid6pvnf3jnBMmnTrgebUqO+8Dl++vAeo6jZkFCk4Wr/t45IITQXoBNcPUl+Fcb8Nu1eDV7+1oXkNEUqyA+b851euzJDVPjl+plt9xXdXz8tgtGgxZZMPcHQ50TRCx6qATB84peOtudftLs9ZL0kjquoH1ryG9iMjtinsBO3fu9BgABACNRoNXXnkFt9xyS6UGriJGdDbAbFPw5reFyLMoaFNHg1lDPe+QnbwoI7vEItq9W+hxsVDB9A1mZBQ/OjxraIhr+vaBsw7sLX5T050f5Hr83vpxYYiLkLB2vw0XCxV8t9eG7/a6Z3DEhovY+FK4H2N8beo2b4Nxn69z/T3oH+pA0bblCzD/1ScDFawK6d1YxMUiBdO3OpFRCDSJETDrAcn12OPZfEAQ3Ld3W9cS8W4f4MMtTnywRUZ8BPDx/RIaRbvzxh2NREy8E/h0uxP/3gjUiwA+uk9Cm+ILowMXFOw9px7zztmed+/WP6VBnH+X8/LKeWgVYIqEptNYCEFRUNL/gHXJMKBIfVmIEBoLKO78LgTHwPD4atff2nZPQdvuKThPbofty4dc34vxnSCG1YLtOr8VuKQRnfQw2xW8ubLIXZ4fDfIsz9lOZBe5O7u9m+vU8rzRjIwC9fGUWY8FeTyO8dpdRogC8Pxi9S2hnRK0ePMez1kiU+8PwuQ1ZoxaVAhBAG6N12DWY57LAKxIteHtH81QFCC5tgYLHg9GUlzlrh/Xu5lWzec/2dT4VBcx62GjKz5n82QIgjturWtLePd+Az7cZMUHm2yIrybi40EGNIpxn6ONRxz4x3fu2VNjl6v/P+Z2LZ7tor4h83+DjXhvoxWjF1tQZFNQJ0LElPv06FKJF4tXY8Sd4TBbZby5KB15RTLaJBgw69lYj9njJzPsyC5wD1zvP2HB0A/Ouv6e8rVaNvq1D8GUYdUhiUDaeRueS8lDdqET4UESWtQ1YNFLtdAw9vq+HRgARtxuVNuxFQVqvq+rxazHwzzzfZYT2UUl2rEkPS4WyJi+vggZ+eqjZrMeD3U9DqzTALvS7Fiw1Yw8s/rygVvitfhydJhr0Xq9Flj6mwVTVjlhcyioESbizuZ6PNnl+ixtMaJHiBrvxReRZ5bRpr4es0ZGe8Y704HsEoN0vVubcLHQiek/5CIjz4kmtXSYNTLa9XioVhLwv5FReG9lLkbPylTzcpQGUx6uhi5N1XhtPWzFiUwHTmQ60GXiOY8wHfqw/LWJr1XvZpri8m1HRqFNLd8PGVyPQZ3N9VK+++nx4WZbcfkW8PEgPRrFqNtcyFew8Yia9/vN8nwb7vxHDWgXL8GgFbDukAPTf5ZhtqmP2HVuIGF0J22Zl8T4k5resmd6j4oplc8dyC50l+XerYPUfL66OL3jdJg1KgZRJQZrj6c78MH3OcgtkhFbTYNRd4ZiWFf3c4sGnYB1+4ow/YdcmG0yokMldG5ixOg7Qys9/r2ba9X2aJO1uD0SMetRk7v+Lp2+dTR4d4ARH2604oMN6iDLx4ONaFRiHdYRt+nUcrLSUtwuSpj1qMnrEyrlkWUFK1LtuD9ZW6k3MHs3K47zZmuJNqtknBUIgrsMt64t4d3+xW3WRkWN84NGjzZrREcdzDbgze9LxPkRk8eTGC/31EMS1BtjFruClrUkzHvMiLDiQZD6URL+O9iIGT/bMHhuEUQBav/gEZPHkiOVzXVd8k2J+nxYaNnrkhKPT/ZO0uNioaxel1yqz4eVvC5xYu8ptQ965/s5Hr+3/qVwv9+0qUgeVONUoq6+TFkAgNd6GSAKFrVv5gQ6NdDgzXvcA2KSKGDmwyZM+t6CwbMLYdQJ6NdSi+e6udtpWQE+S7EhLVOGRgLaxWvw5XAT4kos05Ker+D+/7nXlJ+7zYa522xoW1fCwsd9rClUAf7I+xsPO/CP79x1+dhl6v+PuV2HZ7uq8f5qpx0zfnZfez46X13D8T99Da4ByOupd1NJbde22N3XZ4N07uuzPMXjbdWt40S821eLD3924IOfHIiPEPDxAC0a8S3ARNeNoCjKFU2Kr169OhYuXIg777zT4/u1a9diyJAhuHDhwlUFRFna4ar2+7sb9fq1P8L5dzTzlYaBDkJAWLIKL79RFWSoGxPoIASGw0/P2t3oatW4/DZVUXZOoEMQGMabdD3czIuX36Yqio4MdAgC42Yt39dzTYgbif4mfezQbr/8NlXRzZrPb9L0FobtDXQQbgjvtrpJ67kSXtpju/xGVdAVD7k/+OCDGD58OBYvXoxTp07h1KlT+OqrrzBixAg89NBDlz8AERERERERERERXVdX/JzXu+++q64PMWQIHA4HFEWBTqfD6NGjMWXKFH+EkYiIiIiIiIiIKoGAm3QGLF35IKBOp8O0adMwefJkHD16FADQoEEDmEx8Oy4REREREREREdGNqEKDgP3798e8efMQGhqK/v37l7ttcHAwmjVrhlGjRiEsLABvTSAiIiIiIiIiIiIPFRoEDAsLg1D8Wp/LDexZrVbMnDkTW7duxXfffXftISQiIiIiIiIiIqJrUqFBwM8++8zr//ty8OBBtG3b9upDRURERERERERERJXmitcErIjExERs27bNH4cmIiIiIiIiIqKrJAqBDgEFiuiPg0qShJYtW/rj0ERERERERERERHSF/DIISERERERERERERDcODgISERERERERERFVcRwEJCIiIiIiIiIiquL88mIQIiIiIiIiIiK68XA22M2LaU9ERERERERERFTFcRCQiIiIiIiIiIioiuMgIBERERERERERURXHQUAiIiIiIiIiIqIqjoOAREREREREREREVRzfDkxEREREREREdJMQhECHgAKFMwGJiIiIiIiIiIiqOA4CEhERERERERERVXEcBCQiIiIiIiIiIqriOAhIRERERERERERUxfHFIERERERERERENwnOBrt5Me2JiIiIiIiIiIiqOA4CEhERERERERERVXEcBCQiIiIiIiIiIqriOAhIRERERERERERUxfHFIERERERERERENwlBCHQIKFA4E5CIiIiIiIiIiKiK4yAgERERERERERFRFcdBQCIiIiIiIiIioiqOg4BERERERERERERVHF8MQkRERERERER0k+BssJvXjTMI2PGRQIcgIGa+kh/oIATEqLf/DHQQAmLGs3UDHQS6nqSbtHnNyAp0CAJDpwt0CALDYgl0CAJDqw10CAIjJy/QIQgMWQl0CAJDkgIdgsCwWAMdArqe9Ddp+22zBToERBQAN+kVKhERERERERER0c2Dg4BERERERERERERVHAcBiYiIiIiIiIiIqrgbZ01AIiIiIiIiIiLyK1EIdAgoUDgTkIiIiIiIiIiIqIrjICAREREREREREVEVx0FAIiIiIiIiIiKiKo6DgERERERERERERFUcXwxCRERERERERHST4HtBbl6cCUhERERERERERFTFcRCQiIiIiIiIiIioiuMgIBERERERERERURXHQUAiIiIiIiIiIqIqji8GISIiIiIiIiK6SYh8M8hNizMBiYiIiIiIiIiIqjgOAhIREREREREREVVxHAQkIiIiIiIiIiKq4iq8JuBHH31U4YM+99xzVxUYIiIiIiIiIiKiG82MGTPwzjvv4Pz582jZsiWmT5+OW2+91eu2Xbt2xU8//VTm+969e2PVqlUAgGHDhmH+/Pke/96rVy+sWbOm8gNfrMKDgB988EGFthMEgYOAREREREREREQ3IIEvBrliixcvxtixYzFz5ky0a9cOH374IXr16oXDhw8jJiamzPbLly+HzWZz/Z2VlYWWLVti4MCBHtvddddd+Oyzz1x/6/V6/0UCVzAImJaW5s9wEBERERERERER3XDef/99PPnkk3j88ccBADNnzsSqVaswd+5cvPrqq2W2r1atmsffX331FUwmU5lBQL1ejxo1avgv4KVwTUAiIiIiIiIiIrppWK1W5OXleXysVqvXbW02G3bt2oWePXu6vhNFET179kRKSkqFfm/OnDkYPHgwgoKCPL7fvHkzYmJikJiYiNGjRyMrK+vqI1UBFZ4JWNrp06fx3Xff4eTJkx5THAF1hJSIiIiIiIiIiOhGM3nyZEyaNMnjuwkTJmDixIllts3MzITT6UT16tU9vq9evToOHTp02d/69ddfsX//fsyZM8fj+7vuugv9+/dHvXr1cPToUfzjH//A3XffjZSUFEiSdOWRqoCrGgTcsGED+vbti/r16+PQoUNo3rw5jh8/DkVR0Lp168oOIxERERERERERUaV47bXXMHbsWI/v/LUe35w5c9CiRYsyLxEZPHiw6/9btGiBpKQkNGjQAJs3b0aPHj38Eparehz4tddew0svvYTff/8dBoMBy5Ytw6lTp9ClS5cyzzcTEREREREREdGNQeQHer0eoaGhHh9fg4BRUVGQJAkXLlzw+P7ChQuXXc+vsLAQX331FYYPH17udgBQv359REVF4a+//rrstlfrqgYB//jjDwwZMgQAoNFoYDabERwcjLfeegtTp06t1AASEREREREREREFgk6nQ5s2bbBhwwbXd7IsY8OGDejQoUO5+y5duhRWqxWPPvroZX/n9OnTyMrKQs2aNa85zL5c1SBgUFCQax3AmjVr4ujRo65/y8zMrJyQERERERERERERBdjYsWMxa9YszJ8/H3/88QdGjx6NwsJC19uChwwZgtdee63MfnPmzEG/fv0QGRnp8X1BQQFefvllbN++HcePH8eGDRtw3333ISEhAb169fJbPK5qTcD27dvjl19+QZMmTdC7d2+MGzcOv//+O5YvX4727dtXdhiJiIiIiIiIiIgC4sEHH0RGRgbefPNNnD9/HsnJyVizZo3rZSEnT56EKHrOszt8+DB++eUX/Pjjj2WOJ0kS9u3bh/nz5yMnJwexsbG488478a9//ctvaxMCVzkI+P7776OgoAAAMGnSJBQUFGDx4sVo2LAh3wxMRERERERERERVyjPPPINnnnnG679t3ry5zHeJiYlQFMXr9kajEWvXrq3M4FXIVQ0C1q9f3/X/QUFBmDlzZqUFiIiIiIiIiIiIiCrXVa0JWL9+fWRlZZX5Picnx2OAkIiIiIiIiIiIbhyiwM/N6qoGAY8fPw6n01nme6vVijNnzlxzoIiIiIiIiIiIiKjyXNHjwN99953r/9euXYuwsDDX306nExs2bEB8fHylBY6IiIiIiIiIiIiu3RUNAvbr1w8AIAgChg4d6vFvWq0W8fHxeO+99yotcJVFURRMn7cDS1cdQF6BFa2b18SEF7ohPi68Qvt/+sVOvD87BUP6t8Q/nrndv4GtoEW7nZjzm4zMQqBxjIA3eohIqul7YueawzKm/eLEmVygbgTwUhcJXep7bn80S8G7Pznx2ykFTgVoECngo/skxIYKyDErmL5VxtbjMs7lA9WMQI+GIp7vJCJEf+PPpW14SyfcOWIs6jRrhfDqsfjk6YHYu/67y+94g/piH/DZHiCzCEiMAv5xO5BU3fu2f2UB03cABzOAs/kCxndSMCTZc5udZ4C5e4CD6UBGkYCPeivocQM82a8oCqZvsmDpLhvyLApa19FgQh8j4iOlcvdbtMOKOdssyCxQ0Li6hDd6G5EU567urHYFU9easWq/HXangtsaaDGhjxFRwWqZWL7Hin98Y/Z67K0vhyIy+KomUXsP6682zNlqVcNaQ8QbdxuRFOc7fmsO2DFtoxVncmTUjRTxUk89ujTSuv5dPWdWLN1tV89ZbQkT+hhc5+x0toz//mzF9jQHMgsUxIQIuDdJi1Gd9dBp1LJstSuY8L0FB845cSxDRtdGGsx4yFRpcfZFURRM32jB0p1Wd3r3NVUgvS2Y84sVmQUyGteQ8MY9Jo/0XvybFd/vs+HgOQcKrcCv/whDqLFsGm4+bMcnm804fN4JvUZA23gNZjwSXOnxLE1RFExfV4ilv5qRZ5bROl6HCfeHID6q/CZ60bYizPm5EJn5MhrX1OCN+0KRVFtbZjtFUfDU3BxsOWLDx0PC0LOZAQCwfKcZ/1ia5/XYW/8ZXan53Bs13kVY+pulON5aTOgXfPl4p5gx56ciNb1ravBG32CPeL+5PB8pf9mQnifDpBfQqo4WL90dhPox7uOm/GXDtB8LceS8EyYd0K+NAS/cGQSNVPntWaDqMQBoPCGnzHHfe8CEe1roAAC7Tjjw7jozjmXKsNgVxIaLeLCNDsM6Gion8iXcDPW51/D/ZsOcbbbi8It4424DkmqVU8cftGPaJpu7ju+hR5eG7vj++IcdX+2y48A5J3LNwIqnTGhSw328HLOC6Zut2HrMgXO5CqqZBPRorMHzXfUIMVy//log833KMTumbbTgyAUnTDoB/Vrq8EIPg1/Kd5nw/2bDnBS7O73v0l8mvR2YttmKMzkK6lYT8VIPXan0duCr3SXS+0mjR3oDwMmLMt5eb8WuU07YHEDnBhq8cZfO45z4WyDi/diCIvx2Qvb47sHWGky6p/LrL18URcH0DeZS/ZYgxEddJp9vt2DOLxZ3v6VPUKl+iwXf7y3Rb3k93KPfsuOYHUPn5ns99tJRoWgRd1XL/lfYol1OzNnhcF+H3qFBUmw516GHnJj2sxNnchXUrSbgpa4SujTwfo4mrLFjcaqM13pIGNrWMx6b/3Lik61OHM5QoJeAtnVEzBhQtt9DROW7otZBlmXIsow6deogPT3d9bcsy7BarTh8+DD69Onjr7Betdlf7cbC5Xsx8cVuWDJjEIwGLUaM/xZWm+Oy+/5+6AIWf38AifUjr0NIK2b1IRlTNssY01HC8iEaJEYDI5Y6kVXo/a0zu8/IGLfSiQdaiFgxVIOeDUU8s8KJIxnu7U9mK3j4CwfqVxOwYLAG3w7V4OkOIvTF9XN6AZBeoOCVrhJWDtNg8t0StqTJeH1N2cfCb0Q6UxBOH9qHL996PtBBuWY//Am8/QvwdFtg6YNAYiQw8jsgq8j79mYHUDsMeLEDEGXynkfMDnUw8Y0ufgz4VZj9ixULd1gx8V4TljwZAqMWGLGwEFa793gAwOr9NkxZa8aYrgYsHxmCxBoSRiwsRFaBu6M4eY0Zm47YMW2QCQseD0Z6voxnvyp0/Xvv5jpseSnU49MpQYO28VKlXjCu3m/HlLUWjOmqx/KRQUisLmHE555hLWn3SQfGfW3GA621WDEqCD0ba/DMV2YcueAuh7O32rBwhw0T+xiwZEQQjDoBIxYWuc5ZWqYMWQEm9THi+6eD8VovAxbvtOODDVbXMZwKYNACj7XToUP98juylWn2FisWbrdiYl8TlowMUcM+v6D89P7dhik/mDGmmwHLR4eq6T2/wOMcWuwKOjfUYuTtRp/HWXvAhvHLCtG/lR7fjAnFF0+GoE+SrlLj58vsn4qwcGsRJt4fgiXPVFPjPSen/HjvtWDK9/kY0yMYy5+LRGJNLUbMyfaad+b/UgTBy7Vv75YGbHkjyuPTqZEObetr/T4wAgCzfzJj4TYzJvYLxpIxETBqBYyYm1uBeBdgTM8gLH82Aok1NRgxJ9cj3s1qafCfB0Kwamw1zH4iDAqA4XNy4ZTV4x4668BTn+WicyMdVjwXgfcfDsXGgza8t6bQx69eYzwDVI9d8p9+Ro+6rGdj9wWTUQc80k6Pz58IxqpnQjD6dgOmbbRg8U5rmeNcq6pen3sN/wE7pvxoxZgueix/yqSGf1ERsgp91PGnnBi3zIIHWmmx4ikTeiZq8MxiM46ku+t4sx1oU1vCSz30Xo+Rni8jPV/BKz0NWDkqCJPvM2DLXw68vtLilzj6Eqj0PnTeiac+L0TnBC1WjArB+wODsPGwHe+t93/8Vx+wY8o6G8bcrsPyJ01IrC5ixBfm8tN7uQUPJGux4kkTeiZKeGaJpVR6K+Wmd5FNwfAvzBAAzHvUiC+GGWF3Khi92ALZx9soK1sg4n3JwFYabHnR5Pq83LP87Svb7C0Wtd9yXxCWjAot7rfkX6bfYsWUH4owppsRy58OQ2INDUbMyy/Vb0G5/ZZWdTTYMj7c4zOwjR5xESKalzP4WhlW/+HElI0OjOmkwfLHtUiMETBisd33dehpGeO+deCBliJWPK5Vr0OXOXAko2z+WHfYib1nFcR4uf+69pAT4793oH+ShG+e0OKLx7To0/T6DXQTVSVXVXLS0tIQFRVV2WHxC0VRsGBZKkY92hY9bquPxAZRmPrqHUjPLMT6X46Vu2+h2YaX/vMj/jWuG0JDrt9dpcuZt1PGwCQRA1qISIgSMOlOCQYtsGy/98Z24S4ZneoJGH6rhAaRAp7vJKFpdQGL9ri3//AXJ7rUF/ByV/Xf6kQI6J4gIjJIvXJsFC1gej8NuieIqBMhoH1dES92lrDpqAKHfH06GdfiwM9r8e2HE5G67u87+++S+anAA82A+5sCCdWACd0AgwZY/of37VtUB166DejdCND56Bd0rgs83x7o2cBvwb5iiqJgwXYrRt1uQI/GWiTWkDC1fxDS82WsP2T3ud+8bVYMbKPDgFZ6JMRImNTHqJaPPTYAQL5FwbI9NozvZUT7+lo0j9Vgcj8T9pxyIvWUemPAoBUQHSK6PpII7Ehz4IHWldu5nJdixcDWWgxopSsOqwEGrYBle7zHb+EOGzolaDD8Nj0aREt4vrsBTWtKWPSrrcQ5s2HU7Xr3ObvfiPR8BesPqXHr3FCDyf2M6JSgQe1qIro31uKJjjqs+8P9myadgIl9jBjU5vrNJFAUBQtSLBjVxYAeTXRIrKHB1AHF6f1HeeltwcBb9BjQuji97zWp6b3b5tpmaEcDnrrdgJa1vRcAh1PBf1YX4eVeRgy+VY96URISYiTc3cL/g4CKomDBL0UY1T0IPZoZkFhTi6mDQpGe58T6A74HYuZtKcTAW40Y0NaIhOoaTLo/RM07v3nOePrjrB2f/VyEfw8MLXMMNZ9Lro8kCNhx1IYH2voeLK0siqJgwVYzRnU3oUczPRJrajD1wRCk58lYf7CceP9ixsBbDRhwi0GNd79gGHQClu10X+A/2M6ItvV1iKsmoVktLV64MwjncmWcyVbbvNX7LEisqcGYnkGoGyXh1vo6vHR3EL5IMaPA6r0dvaZ4BqgeuyTU4Fmf6bXuEeGmNTXo00KHhjES4iIk9G2pQ6cELXaduPxN0r/Lebhe9bnX8KfY1Do+WYuEaAmT7tFXoI6XMLyjTq3ju+nRtKaIRb+5t78vSYsxXfToUN/7TJ9GMRKmDzKie6IGdaqJaF9Pgxe767HpiOO69dcCmd6r99uQWF3CmK4G1I2UcGu8Bi/dacQXv1pRYPVv/Odtt2Ngq0vpLbrTO9V7eVr4q71Eeou+0/t2HTrU895+7T7lxJkcBZPvMyCxuoTE6hKm3GfA/rMytqddnxv1gYj3JUatgOhg0fUJvo5PJymKggXbLBjVtUS/5YFL/Rabz/3mbS3ut7Qpzud9i/stu9xt39COBjzVxYiWtb2Xc53Gs14LNwnYcMiG/q31ELzd9atE8351YmBLEQOSJCREiZh0l0YN/z7v+W3hTic61RcxvJ0GDaJEPH+7Bk1rCFi0y3P7C/kK/m+9A+/cq4GmVNfTISv4zwYHXu6mweBWEupVE5EQJeLuJtfvRnVVJAj83KwqfHX30UcfwWKxuP6/vM+N5PS5PGRcLELHNrVd34UE65HUpDpSD54vd9+3pv2Eru3i0bFNHX8Hs8JsTgUHzivoWNeda0VBQIe6AlLPeu/cpJ713B4AbosXkHpWvdiRFQWbjyqIjxAwfKkDHWfYMehzB9b/Wf7FUL4VCNYBmpv51TrXmc2pPrLbwZ2dIQpA+zhgb/nZ+W/ndLaMjAIFHUtc6IQYBCTVkspc5F5icyg4cM7psY8oCuhQX+Pa58BZB+xOeGxTP1pCbJjg87jfpNpg0AK9mlbeIwc2h4IDZ2XvYT3tvSOVesqJjqVm5t2WILm2P52teD9ncZLPYwLqhVWYMbDl2JXeDUqHXVN+ep/1kt4NtD738ebgOScu5CkQBOD+GXnoPDUHTy7I95hh6S+nLzqRkS+jY0P3gGOIUURSbS1ST3q/iLA5FBw44/DYRxQFdEjQIfWk+yLKbFPw0pe5eLNfCKJDLt9R/ma3GQatgF4t/H/T6/RFWY13Qol4G4rj7WMAyhXvhNLx1iL1hPeBhSKbguU7LYirJqJGmFh8HEBf6rrKoBVgdQAHTlfu4NeNUI+9tcqM9lNzMfDTfCzbbYVSzuygg+cc2HPKgbbxlfso2Y1wHi7xR33uNfxOBQfOyehYYhBDFAR0qCch9bT3/lXqaSc61vM897c18N0mVFS+RUGwXrhu/bVAprfX8q2BWr7PVm759gh/uento00/7fTYHgBuq+87f3j/XUCA501evUbtG+465f82LFDxvmTlfjvav1uAe2cW4b0NVpjLmYFX2dz9FnddEmIQK9ZvKbHP1fRbStt4yI6cIgX9/Xxzw3UdGu8eQhAFAR3iRaSe8XUdKqNjfKnr0Hqe28uKgldW2jH8VgkNo8sOTxw8r+BCvjpoc/9cGzpPt+LJJTavswmJ6PIq3Mv74IMP8Mgjj8BgMOD999/3eZdBEAQ899xz5R7LarXCavW806+z2qHXV36HLOOi+oxkZITnelZRESZkXvT96M+qjUdw8M8MfP3fQZUepmuRbVYf1YsstTxXlElA2kXvlW9mIVwz+lzbB6nfA0BWIVBkB2b9KuP5TiJeul3CluMynv3GifmDgVtrl62Ms4sU/DfFiUEtOQ37esoxA05FQKTRM60jTUBaTmDC5C8ZBWocSz+uFRUsIrPAe17PLlLglL3vk5bpcB1XK6HMmnCR5Rx32R4b+rTQwaCtvAuo7CJ17c3I4NJlU0BapveOc2aBUjZuQe5wZxQ/SuLtmJk+HjE+kSXj819teOXOwM529pne5YTdd3r7PofenLqoHn/GRgvG321ErXARn221YMjcfKx5PhThJv/Vcxn5l9LMSz7P9xVv2Xu8Q0SkZbgHDievzEerulr0aFaxtF32mxl9kg2Vms998ZlXg8Vy0ttHvINFpGV4DgJ+kWLGuz8UoMgG1IuWMHd4uGvNy06NdFiw1YzvUy24O0mPzHwZn2xQ+woZPs751Qp0PfZcNwPa11dnaWz9y4FJq8wotAFD2nteKHZ5LxcXC9XffaarAQPbVO6FZKDPQ0n+qM+9cdXxQWXrtPLreG/199UPbGQXyfjvFhsGtb5+62YFMr07JWiwYLsV3/9uw93NtMgsUPDJTxaPcPlD+W2693ols0Ap2z8PFpFZWPHBoORaEow64N0NNrzYXQdFAd7baINT8W98LwlUvAGgT3MtYsMExAQLOJIu490NNhzPkjF9kP9nswMl2zFv7ffl8nnZ+Kdl+p4leznLdlnRqaHWdbPLX7KLiq9Dy1xXCkjL8pXe3rfPLPH48KztTkgi8Ngt3m9YnspRt53xiwPju2tQK1zAZ786MeQLO9Y8pUN4gG9kE/3dVHgQMC0tzfX/x48fv6YfnTx5MiZNmuTx3Zsv3o2J43pf03EBYOX6w5jw/ibX3zMn33vFxziXno//zPgZc9/uB73Ovwur3gguVdndEwQMK658m1SXsOeMgq9S5TKDgAVWBSOXO9EgUsAzHTkISJVj5T4bJqx0L2w48zq8kKEi9pxy4GiGjKn9r8/6cNfThTwZT35eiLuaajGozfWN38q9Vkz4rkR6Pxq49L70hNzILgb0aqaeh8n9g9DlnVysOWDH4LaVNyCyco8ZE5a7F/Oe+Xh4pR27pI0HLdhx1Iblz1er0PZ7TthwNN2JqQ/65+Jp5R4LJqwoEe9hYX75nUvubaVHx4ZaZOTJmLvFjBe+yMOXo8Kh1wro1EiHl3sHYeKKAoxfkg+dBIzuEYSdx+241olSN1o99nRX9wBw05oamO0K5m61lBkEXPREMAptwN5TDry33oI6kSL6XMPj8DfaebikKtfn3hRYFYz8wowGUSKe6eK/ON9I6d0pQYuX7zRg4soijF+uzpAb3cWAnSec11y+b0TVggR8OMCAST9YsfBXtQ67p7kGTWuIVTK+JT1YYmA7sbqE6GABwz634ORFGXWqVf71ycpUKyZ8555AMvOxkEr/jatxPlfGL3/a8cHgG6OevVL7z8tYuNOJZcN0PicZufppHTXo1Vi9Vp3cW0CXGTasOSRjcCs+Fkx0Ja54hMtut6Nx48b4/vvv0aRJk6v60ddeew1jx471+E6XOeeqjlVat471kNTE/ZpUm02925qVXYSYyCDX95nZRWiSEO31GAeOpCMr24z+I79yfeeUFezcdwaLvtmHfWufhiQFZvArwghIQtmXQGQWKYgK8r5PVBDKLNaaWQjX9hFGQCMCCZGeFW+DSAG7TnvuV2BTMOJrJ4K0wMf9JGivw5vWyC3cCEiCgqxSLzrMKgKi/P/yVr/qlqhFUi13h6q46CKrQEZMiLu8ZRbIZd4Qd0mESYAkoszLETILZEQV33WNDhZgdwJ5ZtljNkFWiW1K+nqXDU1qSGgeW7k3BCJMglqWC0qXTcXnOnxRwULZuBWWjJu6X1aBgpiQktsoZc7ZhTwZQ+YVoVVtDd669/rPAuzWWOfxJrxL72kqk95ewn6J7/T2fQ69iQ5Rz19CjPt3dBoBtauJOJdTuTPDujXVe7zJ1iPeoe7fzyyQ0cRHnoswid7jnS8jqvix3+1/2XHyohO3Tszw2Oa5hbloU68IC0d6Dg5+/asZTWI1aB7nn9lC3ZrqkFTb/Zs2p5rvswoUxJRYrjCzQEaTmlcY7wK5THqHGESEGETERwEt62jRblIm1h2wok+ymtcf72zCsE5GpOfLCDOKOJPtxPtrClG72rVdSNyo9dglSXEafPKTFTaH4poZCQBxEWpYEqtLyCpU8PEmyzUNAt6o58Ff9bk3rjq+UAZQomxfto731iZceV+rwKpgxKIiBOkFfPyg0a/9tRstvR/vaMCwDnqk56tLXZzJkfH+egtqR/iv715+m+793EcFC2X75wUyooKuLK06NdBg3TMaZBcpkER1HdBO7xeidrj/r1UCGe/SLr2N+ES2fwYBuzXRIal2yX7LpXbMSz6vebl87iX+V7kG8/LdVoSbBHRv7P/ZvhGm4uvQMteVis/0iwouf/tdp2RkFQLdP3E/yeBUgKkbnZj/mxMbn9YjujgvlbxW1WkE1A4XcC7vxl+bnuhGc8W1jVarda0NeLX0ej1CQ0M9PpX1KHCwSYe6tcJdn4T4aoiuZkLK7lOubQoKbdj3xwUkN63h9RjtW9fGd3MexopZD7k+zRNjcG+PRKyY9VDABgABQCcJaFZDQMoJz3UUtp9QkBzrvfJNjhWQctKzgtx2QkFy8avcdZKA5jXKPk58/KKC2BKTNQqsCoYvcUIrAZ/0l6DXcADwetNJQNMYYLs7O0NWgB2ngZbes/PfRrBeQN1IyfVJiBYRHSwg5Zj78ZACi4J9Z5xILmeh5GY1JY99ZFnB9jSHa59msRpoJSAlzb3NsUwnzuYqZY5baFXwwwEbBrSu/BkUOo2AZrGiRzhkWcH2Yw4kx3nvPCbXlpBSaqHvbUedru3jIgT1nKWVOmennR7HvDQA2CxWxH/6GSAGYLpAmfSO8ZHepx3lp3esl/Q+Zve5jzfNYzXQaeDxiJ7dqeBMtozYSr6ICtaLqBulcX0SqkuIDhGR8pe781tgkbHvlB3JdbznO51GQLNaGo99ZFnB9r9sSK6jtqVPdjPh2xciseJ59wcAXr03BJMHes7CK7TK+GGfFQP8+EIQNd6S65MQU06865aT3l7jbUdy3fL7EArcA66XCIKA6qESDFoBq1KtqBkmommtaxscuhHrsZIOnXMizCh4DACWJivuQdqrdSOeB3/W517DLwloVlP0qLNlRcH2NCeS47zXK8lxkkdcAGBbOW2CLwVWBcM/L4JWEvDJYKPf+2s3Ynqr5VtUy/fvNtQME9DUx8BMZXCl93Fv6e2jTY/z0qaXkz8uJ8IkINQgYHuaA1mFCro18v9g940Q70sOXVAHkGOuYtC8Isr2W9TZhylH3Y/xVrzf4t7navotlyiKguW7rbgvWX9dJma4rkOPuwfr1etQGcm1fF2Hikg5Xuo69Lh7+77NJXw7XIsVT7g/McHA8HYSZj+otu3NawjQSfC4VrU7FZzJVRAbxuvRqyUK/Nysrqp1GDNmDKZOnYrZs2dDo7mxH5cVBAFDBiRj5uc7EV8rHLVqhuKjz7YjJioIPf+fvfsOj6Jo/AD+3b1+6aQACSVAINRIUyA0pamoqCDK+0pTEOFFRcGCr74gvu9PsNLUl1eKFLEgTVBAqYJ0pCggIkUBaenlcn3398eGu1xyF+Byl0Py/TzPPZC73b2ZndmZ2bnZmU71XdsNHbcCPTrVx8AHb0G4UYtG9WI9jmPQaxAdqS/zfigMbSti/BonmtcQkFZTwIJ9Esx2oG9zpfJ86RsHEiIEjOuiVL6D2ogY/LkT8/Y6cXt9Ed8ck3DkoozXe7kr52G3ihi72om2tSS0qyNg22kJm0/KWDhA2abQKmPYl06Y7TLevkeNQitcq6xVMwKqG/wq0hnDEF/XvfRtXK1k1GqSBlNuDnIunC1nzxvPkJbAPzcAzRKUlX8XHQLMDuDB4oG5L68HEsKA59KVv21O4GS28n+7E7hsAn7JAIwaoG608r7JBpzJc3/HuXxlmyg9kBiipx0EQcDg9jrM2mpFcqwKSTEiZmwyIyFCRI8Sv3YOnV+IHk00GNhOebxtaLoO41cUoXmSGmlJKizYaYXZBvRtpdz4RegF9GulxZvrzIgyCAjXCfjPGjNa1laVaYCtPWyDUwL6pAXn19WhHXQYv8KM5okqJay7bDDbZfRtpXzfS8vNSIgUMK6HMnppUDstBs8vwrwdVtzeUI1vDttx5LzTNZJPOWda5ZxVE4vPmRUJEQJ6NFbidqUDMDFKwEu99Mg2yVC6SID4Er9kn7jshN0J5JllmGwyfrmgNNh9/bpdUYIgYHAHPWZtsRSHXYUZG4vTu0mJ9P64QEnv9kqch6brMX65Cc2TVEhLUmPBTouS3iVu9DMKJGQWSjhTPF/N8UtOhOkk1IwSEW0UEa4XMOBWHWZuMqNGlIjEKBHztis/dt3VPLi/rAuCgMGdjJi1yYTkOJUS7+9MSIhUoUcz9yObQz/KQY/mOgxMV4b8Du0chvFL8tC8lgZptTRY8EORknfaKudFWfG37PclRouoVWq029pDVjglGX1aVd6IUEEQMLijAbM2FSnxrnYl3iJ6NC0R79m56NFMh4HpSgfl0E4GjP+yQIl3bTUW/GCG2Sajbxsl7GeznFjzkxUdG2pQLVzExTwJs7cUQacR0LWxO0/M/b4InVK1EAVg/WErZn9fhKl/jwx4XRbKcmzTr3ZkFUq4pZYaOjWw46QD/9tmwWPp7vO7eLcVNaNF1I9Trv19vzswb4cFg9oFdk7AqlCeezO0gxbjV1qUMj5RxILdduU6bVlcxq9UzsG47kp8B7XTYvCCIszbacPtDVX45rADR85LeP1e97WZa5ZxIU/C5eI5x67MwxUXrqySeqUD0GwH3n5Qj0KrXKK9JlRKey3U6T33Bws6NdQo1/dRO2b/YMXU/sagx31oew3Gf2VF85oi0hJVWLCnuE6/RQnbSystSvv8SnrfpsHghebi9FbjmyN2Jb3vcV9/V0tvAFh20I4GcSKqGQUcPOfE/31nxZD2Gtd1HWyhiPeZbAlfH3agS0MVog0Cjl+SMHm9FW3riEitXjmPhgqCgMHpxe2WK/nc1W5x1zdD5+WjR1Otu93SUY/xy0xonqhGWi01FuwobreUmIvV1W7JVtpepdstV+w65cC5HAn92wZ/tXNXfG5TYfzXDjSv6Sy+D3Uq4U9TzvtLq+1Ket+upP+gtioM/tSOebsduD1FxDdHJRy5IOP1u5TPYwwCYkrN6acWlSfW6scqcQ3XCRjQSoWZPzhQI1JAYiQwb7dybu5qzKmpiK6XXz14e/fuxcaNG/Hdd9+hRYsWCAvzfA51+fLlAQlcoAwf0Bpmix0T3tuM/EIr2rSoidlT+njM93fmfB5y8io2wrGy9G4sIrtIxsztTmSYgCYJAmY/pHINqz5fAAiC+5eS1kki3rkXmLbNianbJCTHAO8/qEKjeHeB27ORiNd6AR/tcuL/NgH1YoAZ96vQpvhXuSOXZBy6oByz1xzPX6k3jFCjVnCnd6qwus3bYNwn611/P/zPtwEAO5YvxILxT4QqWH65uyGQbQbe36M81t04Hvjffe7HgS8UeC55nmECHvrC/cbHB5TXrYky5vdV3jtyGXhspXubt35Q/n9/Yxlv9Ah6lHwa3kkHs13GhNVFyLfIaFNHjdkDw6ArMaH7mRwncorcDb7ezbXINsmYucmMjELlUdLZg8I8HrN4+S4DRAEY80URbA4ZnVI0mHBP2VFQSw/Y0LOJpswk5IHSu7lGCetma3FYRcweaHSF9XyeBEFwf3frOmq808+AaZusmLpR6eh7f4ABjUo0eId31MJskzFhtaX4nKkwe6DRdc62n3Tgj2wJf2QDXd8r9AjPsdfcz2WOWFyE83nucuTB/5nKbBNowzsXp/eqEuk9ONwzvbMl5BS5w9W7hRbZJgkzN1qQUfwIzuzB4R7p/fleKz7Y7C7fB85V4v3Gg0bXSnov3GmASgReWmqCxSHjllpqzH8sAlFBSvuShnc1Kmm2rAD5FgltkrWY/Xh0qXg7kGNy3zz3vkWvxPu7QmQUKI8Oz348xvU48PVYuteMns31QcvnvgzvalDivbxASe9kDWY/FuUZ7ywnckzuEQdKvGXMXG8qEe8oxBV3YGs1wI+/27FwexHyzcpCOm3rafDZqGiPydu3/mrDrM3K9d+4phofDI5El9Tg3ESFqhzTiMCne2yYvE6ZP6JONREv3WnwmP9TkoGpG8w4lyNBJQJ1qqnwfA8DHmkb+NFyN3t57k3vZsVl/JbiMr66iNl/L1nGyxAEd/5uXVuFd/rqMW2zFVM3yUoZ/4gBjUpMVbDpVwf+ucpdno1dpvx/dBctnr5dhyMXnDj0p3LMXu97LoC34Zkw1IqunB9tQ5neW084MGubBTYH0LiGCh/8LQxdGga/87d3M43SPv/eViK9De70zi9Vp9dW4Z0Hi9N7s01J74f1nul93IF/rnIvpjh2ufL/0V00eLqrUmb9niVh6iYb8swyEqMFjOykxdB2ldfZHYp4a1TAjtMOpcPRBtSMEtCrsRqjOlfufJ/DO+uVeuwrkzufD4ko224pWY+10Cn5fKPZ3W4ZEuHZbtlj8Wy3zFHm1H2jb5jHCsBLf7SiVR016sdX3px4vZuokF0EzNzmcN+HPqJx34fmyx73Ia1riXinjxrTtjoxdasTyTEC3u+nRiMvqwCX54U7VEo7bbUdFgdwS6KA+X/TIEp/Yw9EIboRCbIsX/czH4899li5n3/88cfXHRD5z/eve5+bwtpZoQ5BSIx867dQByEkPni6bqiDEBKq+JhQByE05MDOJ/eXoaqiEzRrq8ZiA2VU1Xxu9X8lR/oLcl77iuM3lapanjuub6Va+ovTVdH6u9B09W1uQsJjP4U6CDeET9Ir78eCG9XAHVWzLefXSEB/OvmIiIiIiIiIiIgoNG7sCf2IiIiIiIiIiChg+CB11eV3J+DSpUuxZMkSnDlzBjabzeOz/fv3VzhgREREREREREREFBh+zY48Y8YMPPbYY6hevToOHDiA2267DbGxsTh16hTuvvvuQIeRiIiIiIiIiIiIKsCvTsAPP/wQH330EWbOnAmtVosXX3wR69evxzPPPIO8vLxAh5GIiIiIiIiIiIgqwK9OwDNnziA9PR0AYDAYUFCgLFs+aNAgfPbZZ4ELHREREREREREREVWYX3MC1qhRA9nZ2ahbty7q1KmDXbt24ZZbbsHp06chy3Kgw0hERERERERERAEgCuy3qar8GgnYrVs3rFq1CgDw2GOP4bnnnkPPnj3xyCOP4MEHHwxoAImIiIiIiIiIiKhi/BoJ+NFHH0GSJADA6NGjERcXh+3bt6NPnz4YOXJkQANIREREREREREREFeNXJ6AoirDZbNi/fz8uX74Mg8GAHj16AADWrVuH++67L6CBJCIiIiIiIiIiIv/51Qm4bt06DBo0CFlZWWU+EwQBTqezwgEjIiIiIiIiIiKiwPBrTsCnn34aDz/8MC5cuABJkjxe7AAkIiIiIiIiIroxiQJfVZVfnYCXLl3C2LFjUb169UCHh4iIiIiIiIiIiALMr07Ahx56CFu2bAlwUIiIiIiIiIiIiCgY/JoT8P3330f//v2xbds2tGjRAhqNxuPzZ555JiCBIyIiIiIiIiIioorzqxPws88+w3fffQe9Xo8tW7ZAENwPVAuCwE5AIiIiIiIiIiKiG4hfnYCvvPIKJk2ahPHjx0MU/XqimIiIiIiIiIiIKplQhRfGqOr86sGz2Wx45JFH2AFIRERERERERET0F+BXL96QIUPwxRdfBDosREREREREREREFAR+PQ7sdDrx1ltv4dtvv0VaWlqZhUHee++9gASOiIiIiIiIiIiIKs6vTsCff/4ZrVq1AgAcPnzY4zOBD5cTERERERERERHdUPzqBNy8eXOgw0FEREREREREREHG1R2qLqY9ERERERERERHRTY6dgERERERERERERDc5dgISERERERERERHd5NgJSEREREREREREdJNjJyAREREREREREdFNzq/VgYmIiIiIiIiI6K9HEEIdAgoVjgQkIiIiIiIiIiK6ybETkIiIiIiIiIiI6CbHTkAiIiIiIiIiIqKbHDsBiYiIiIiIiIiIbnJcGISIiIiIiIiIqIoQuTBIlXXjdAIeXR3qEISEJcsU6iCExAdP1w11EEJi9Mw/Qh2EkJj1n2qhDkJo5BWFOgShUTsx1CEIDYs11CEIjYjwUIcgNAovhzoEoREVEeoQhIapipbnTmeoQxAaqir6sJRTCnUIqDI5quj1TVTFVdEajoiIiIiIiIiIqOpgJyAREREREREREdFNjp2AREREREREREREN7kbZ05AIiIiIiIiIiIKKi4MUnVxJCAREREREREREdFNjp2ARERERERERERENzl2AhIREREREREREd3k/JoT0GQyYcqUKdi4cSMuX74MSZI8Pj916lRAAkdEREREREREREQV51cn4PDhw/H9999j0KBBqFmzJgSBs0oSEREREREREd3o2INTdfnVCbh27Vp888036NixY6DDQ0RERERERERERAHm15yAMTExqFatWqDDQkREREREREREREHgVyfgv//9b0yYMAFFRUWBDg8REREREREREREFmF+PA7/77rs4efIkqlevjuTkZGg0Go/P9+/fH5DAERERERERERERUcX51Qn4wAMPBDgYREREREREREQUbCJXBqmy/OoEnDhxYqDDQUREREREREREREHiVyfgFT/++CN++eUXAECzZs3QqlWrgASKiIiIiIiIiIiIAsevTsDLly9jwIAB2LJlC6KjowEAubm5uOOOO/D5558jPj4+kGEkIiIiIiIiIiKiCvBrdeCnn34aBQUFOHLkCLKzs5GdnY3Dhw8jPz8fzzzzTKDDSERERERERERERBXg10jAdevWYcOGDWjSpInrvaZNm+KDDz5Ar169AhY4IiIiIiIiIiIKHIELg1RZfo0ElCQJGo2mzPsajQaSJFU4UERERERERERERBQ4fnUCduvWDWPGjMH58+dd7/3555947rnn0L1794AFjoiIiIiIiIiIiCrOr07A999/H/n5+UhOTkaDBg3QoEED1KtXD/n5+Zg5c2agw0hEREREREREREQV4NecgLVr18b+/fuxYcMGHDt2DADQpEkT9OjRI6CBIyIiIiIiIiIioorzqxMQAARBQM+ePdGzZ89AhoeIiIiIiIiIiIJE5MIgVdY1dwLOmDEDI0aMgF6vx4wZM8rd9plnnqlwwIiIiIiIiIiIiCgwrrkTcOrUqXj00Ueh1+sxdepUn9sJgsBOQCIiIiIiIiIiohvINXcCnj592uv/iYiIiIiIiIiI6Mbm1+rAr7/+OoqKisq8bzab8frrr1c4UERERERERERERBQ4fnUCTpo0CYWFhWXeLyoqwqRJkyocKCIiIiIiIiIiCjyRryrLr7jLsgxBKLuczKFDh1CtWrUKB4qIiIiIiIiIiIgC55rnBASAmJgYCIIAQRDQqFEjj45Ap9OJwsJCjBw5MuCBJCIiIiIiIiIiIv9dVyfgtGnTIMsyHn/8cUyaNAlRUVGuz7RaLZKTk9GhQ4eAB5KIiIiIiIiIiIj8d12dgEOGDAEA1KtXD+np6dBoNEEJVEXIsoyZ3+Tiyx2FyDdLaF1fh4mPxCI5wXdY956wYO6GPBw5Y0NGvhPvPxGPHreEeWzz3UETPv+hAEfO2JBXJGHF+JpoUksX7OhcM1WrQVC3GwEhLB7y5V9g2/Aa5AuHvG4rxDWEptNzEGq0gBhVC7aNr8O572OPbXQjt0GMqlVmX8f+RbCvnxCUOPjj05+Ajw8AmUVAahzwzy5AWnXv257IAmbuBo5mAOcLBLzUScbglp7b7PsTmHcAOHoZyCgSMKO3jO71gx6NoGnYthN6DR+LOs1aIbp6Ij78R38c2rAq1MG6ZrIsY+YmC77cZ0W+RUbrOmpM7GNEcqyq3P0W77Zg7g9WZBZKaFxDhVfvMSKtlru4+2KvFV//ZMPRCw6YrMCef0Yh0uA5O8KsLWZsOW7HsYtOaFQC9r4SHYwoeg//AQlz90rINAGN44FXu6uQVrPsFAxXrPtVwvTtEv7MA+rGAM93EdG1vmd8TmbJeGerhL1nZTgloEEsMON+FRIjleMO+tyBvec8j/vILQIm9Sz/XAeSLMuYuTYPX+4sRL5ZRut6WkzsX63c8hsAFm8rwNxN+cjMd6Jxkhav9otBWl13+Xwm0463Vubix1NW2BwyOjcx4NV+MYiLdMdt1nd52HLEjGN/2qFRA3un1A5aPEuTZRkz15vw5R6zUm8lazHxwQgkx5VfRS/eUYS5W03ILJDQuKYar94fibTaZc+VLMsYMS8X247b8P7gKPRopnd9tvOEFdO/NeH4RQeMWgEPtNHj2TvDoVb5zm/B4qq/txe46+8BceXX37+Zlfr7rA0ZeU68PyLBe/29LR9HztqQZ5KwYnwimtQOXf29eJ8dc3fakVkoo3F1Ea/eqUVaku/rbN1RB6Z/b8OfuTLqVhPwfHctuqYoecPulDF9ix3fn3DgXK6McJ2A9HoqjO2mQfUIdxkw6gsLjl2SkGWSEWUQ0CFZxLjuWo9tgk2WZcz8rhBf7i6Rz/tGIjn+Kvl8uwlzv7+SzzV49YEIpNXRuj4f9N8s7D1l99jnkfYGTOqn/FC9fG8R/rkk3+uxt0+MR2x44Mq4xXtsmLvdqqRtDRGv3m1AWq1y0vaIHdM3WfFnroS6sSKe76FD10bu/C7LMmZutuLL/XalDqytwsR79R514KytVmw57iiuq4C9L0eW+Z6dpxyYvsmK45edMGoEPNBSg2e76QJ2nS/ea8PcHTZ3nr5bf5U8bcf0zTZ3vLvr0LWhOx/IsoyZW2z48kCJePfWIznWM79uOe7Ah1ut+PWyBJ0auLWuGh88YgAA5BTJeGGFGb9ekpBrlhEbJqBbqhpju+kQrvMv3jM3W/DB9zaP9+rFilj7dLjX7Zf8aMNXh+z47bITANCspgrPddeXmyeu1e7TDrz5rQW/ZUioGSlgZBcd+rZyXxfXG9aKWrzX5lmu3aW7erm2xVpcrolKuVYiD3z3iwOf77fjyAUn8szAiicMaFLD83gZhRLe3mDDjlNOmGwy6sWKeLKTFnc2ua7b2wqRZRkzN5pLtVPDkBx3lXbqLgvm/mBxt1PvDSvVTrXg60Ml2qmvRJdppx4578C73xbh5z+dEAWgVzMtxt9tRJif+ft6hKKdCgAHzsuYtk3CTxdkiCLQJAGY008Fvaby2yxEf2V+tf66du3q6gC0WCzIz8/3eIXSnA35WPR9Pl4bEIslz9eEQStg+AeXYLVLPvcxWyU0TtJiwiO+5zM022S0aaDH8w/EBCPYFaJqfA803V6BY/t0WOffC+nyL9A9vAAwxnrfQW2AlHsWju/fhFx42esm1gX3w/z+ra6X9fOBAADnsW+CFY3rtvY34K0fgH/cCnz5CJAaCzy5Csgqu3A1AMDsAGpHAc91AOKMss9tUuOAV7sGMeCVSGsMw7ljP+Gz18eEOih+mbPNikW7rHitjxFLnoxQrucFhbDavacfAKz52YYpa80YfYcey0dFIrWGCsMXFCKr0F0GWOwyOjfU4MkuBp/HsTmBu5prMeDWyu0sWHNMwpQtEkZ3ELF8kAqpCQKGL3Uiy+Q9zvv/lDHuawkPNRexYrAKPVIEPLVSwvEM9/ZncmX8/TMn6lcDFj6iwldDVfhHBxG6Um3U/mkCto1SuV4vdKncKXPnbCzAoq0FeO3haljyXHUYtCKGz7pcfnrvN2HKihyMvjMKy1+oidREDYb/9zKyCpSbriKrhGEfZkAQgPlPJeDTZ6vD7pQxanYGJMl9XJtDxl0tjRjQMTg3SOWZ830RFm0vwmsPRmDJU9WUfD43t/x4H7JgytcFGN09HMufiUVqTQ2Gz83xyOdXLPihCF6m8cWx83aMmJeLzqlarBhTDe89GoVNR614d23Zhb8qw5z1eVi0pbj+fiFRSf/3L5Zff9tkNK6lxYSHfdR3UOr4Ng30eP7+0M9ZvOaIA1PW2zC6swbLhxuQWl3E8M8svq/vs06MW2HFQy3VWPGEAT1S1XhqiRXHLyvnxGIHjl504h+dtVg23ICZD+lwOkvCP5ZYPY7TLlmFqX11WDvKgOn9dDiTK2PMMqu3rwyaOVtMWPRDEV7rG4klT8cq+XxOTvn5/KAZU1YXYHTPcCx/Ng6piWoMn5ODrEKnx3b92xmw7V/xrtcL90S4Puvd0vOzbf+KR6dGWtxaXxPQDsA1h+2Y8q0Fo2/XYfmTYUitrsLwT0xer0kA2H/GgXFLzXiotQYrRoahR2M1nvrcjOOX3HGbs92GRbtteO1ePZYMD1PO2aIij3Nmc8q4q5kaA27VevsaHLvoxIjFReicosaKJ8PxXn8DNv3qwLsbApP+a47YMeU7K0Z31WH5CKNS5y4uQpbJR7zPOjFumQUPtdJgxQijkqe/MOP45RLx3mHDoj02vHaPDkuGGWHQCBi+uAhWhzve3/5ix0srzejbUoOVT4bh08eMuLe5uxNFFIDuqWp8OMCAdaPDMLmPHjtPOTHxG0uF4tswXsS2ceGu16ePG31uu+d3B+5prsGCIWH4fFgYakSJGLbIhEv5vsu0a3EuR8LIT4twWz01Vo4Mw+D2OvxrlQXbTjj8DmtFrDliV8q1Llosf8KolGufmsvPA8steKilBiueMKJHqgpPLbF45AGzXUab2io83913G+ylr6w4nSXhw0f0WPWkET0bq/HcMguOXnD63CfQ5myzKO3U+8OwZGRkcTu14CrtVCumrC3C6DsMWP6PKKTWUGP4/IJS7VSU2069lC/h8Y8LUCdWhS+ejMScIRE4cdmJl5cHv/4OVTv1wHkZTyx1omOygCUDVfhyoAqPthQhsv/Pb4LAV1Xl1x1eUVERnnrqKSQkJCAsLAwxMTEer1CRZRkLN+dj5J3R6J5mRGqSFm8OjsflPAc2HPLRMwSgSzMjnr0vBj1LjR4o6f7bwjH67mh0SNX73CZU1LcOh/PQF3D+vBRy1gnYv30FsJuhbtHf6/byxZ/g2DIZzl++huy0ed0G5mzAlOl6qVK6Qcr5HdLZ3UGMyfVZcBB4qBnwYFMgpRow8Q5ArwaW/+J9+xbVgec7Ar0bAVof7f7OdYEx7YEeDYIW7Ep1ZOu3+Graazi4/q8z+u8KWZaxcKcFI7vq0b2JFqk11HizXxguF0jY8Ivd537zd1jQv60O/VrrkJKgwqT7jNBrgGX73Xl9SLoeI7rocUtt3zeAz3Q3YGi6Ho2qV95IOACYv09C/xYC+rUQkRInYFJPUQn/Ye+Nq0X7JXSqJ2DYbSIaxAoY00mFptWBxQfdjclp2yR0rS/gha4qNK0uoE60gG4pImLDPGs/gxqIDxNcL39HS/hDlmUs/D4fI3tFoXuL4vJ7YCwu5zmx4Wff5ff8LQXonx6Ofu3DkVJDg0kPV4NeK2LZLqUhvP+0FX9mOzD50VikJmqRmqjFlEdjcfisDbt+c98EP9M7GkPviESjxMod4S7LMhb+UISR3cLQvZkeqTU1ePPhSFzOd2LDEd836fO3mdD/NgP63WpASnU1Jj0YAb1GwLK9Zo/tfjlvx8dbi/B//cuODlrzkwWpNdUY3SMcdePUuK2+Fs/3jsCnO4tQaK3YTer1ctXfd0Wj+y1hSvoPiVfS/6r1dzX0bFlO/d0uAqN7x6BD49DX3/N329G/lRr9WmqQEi9iUm+tkm4HvZdpi/ba0amBCsM6aNEgTsSY27VoWlPE4n3K9hF6AfMeNeDupmrUjxXRspYK/7pLiyMXJJzPc6fh0HYatKylQlK0iNa1VRiRrsGhcxLsTt83qoEkyzIWbivCyO7h6N5cj9REDd4cEFWcz313yszfWoT+7Yzod6tRyed9I5Xztccznxs0AuIjVa5XuN7dvNWX+kwlCth90oaHbgtsh8j8nVb0b61Bv1Zape65V6+E9YCPtN1tQ6cUNYZ11KFBvApjuunRtKYKi/codZUsy1i4y4aRXXTo3liD1BoqvPmgAZcLZGw45u7seeYOPYZ20KFRgvcm/ZrDdqRWFzH6dh3qxoq4LVmN53vq8OleGwqtFU//+TttSrxbapASr8Kke3TXEG8VhqVrlXjfoVPy9F67O9677RjZWYfuqRqkVlfhzQf0HvF2SDLeWGfFCz11GNBWi3qxIlLiVbi7mbv8jjII+FtbLVokKvm+Q301/tZWgx/PVKyDSCUC8RGi6xUT5vtW6p1+Rvz9Ni2a1FShfrwK/+mjhyQrIzOvsDlkvPmtBV3eLUCr/8vHw7MLsfu0w+cxAeDzfTbUihYx/k49GsSrMLCdFnc2VWPBTs8643rCWhHzd9nRv5XGXa5dyQMHvcdj0R57iTwglskDAHB/mgaju2jRoZ7vNtjBs04MvFWDtCQVaseIGNVZiwg9cORi5dRfsixj4Q4LRt5eop360JV2qo/7KwDztxe3U9sUt1P7FLdTf3Sn35B0PUZ0NeCW2t5HNW751Qa1CEy414j68Sq0qKXGa32M+O6IHX9kBbcTNFTt1CmbnRjUWsCIdiIaxgmoX03A3Y1FaNVVuCeHyE9+1QYvvPACNm3ahP/+97/Q6XSYM2cOJk2ahMTERCxcuDDQYbxm57IcyMh3Ir1EQz/CICItWYeDv1fuL96VRtRAqNEczj9+KPGmDOfv2yEmtQ7Yd6iaPgDnT18G5ngBYHMqj+x2KPHEnigA7WsBhy6GLlwUOOdyJGQUykhv4G4ARegFpNVS4+BZ7w1Lm0PGkfNOpNcvMRpAFNChgcbnPjcSm1PGkUtAel13g0YUBHSoI+Dgee+Nq4PnZY/tAaBjsnt7SZax5ZSM5BgBw5Y6kf6BAw9/4sCG38o2klf/IqP9Bw7c97ED7251wlzOL9mBdi7LiYx8CemNSpXfdXU4eNp7+W1zyDhy1uaxjygK6NBIj4O/21zbCAI8Gok6jQBRAH48VbERIYFwLtuJjAIJ6Q3do3giDCLSamtw8Iz3mwibQ8aRPx0e+4iigA4pWhw8476JMttkPP9ZHiY8EIH4iLI3UjYHoCvVeNZrAKsDOHKucq8XV/2d6qX+9pH+fzU2p4wjFySkl7ipFQUBHZJVOPin95vWg+c8tweAjvVVOHjO901ugQUQAETqvd8Y5ZplrD7sQKtaIjSV9Ni3z3xeR4ODf3jvLFLyub1sPm+oLbPP6gNmtJ94Cfe9k4l31xTAbPNddq380Qy9RsCdaYHrFFbqHqls3VNfjYPnvN+QHzzrRHr9UmmbonJtfy5HVurA+qXrQJXPY3oNm9PLda4WlOv8fMU6C3zm6Xq+8+jBc06k1/Ps2OjYwH2ezuVeibf7mBF6AWlJ7ngfvSDhUoEMQRDw4EcmdH6vEE8sLvIYSVbapQIJ6485cGvdiv2w90e2hM7vFKDHtAI8v6wI53OvvcPJbAccktJBecW/11hw8JwT7z1kwFejwnFnUw2e+KQIv5fTkXPwrBMd6pc6hyll81pFwnqtys8DPvL+Oed1l2vetKytwpqjDuSaZUiyjG8O22FzALdVMI2vlbud6u58jtCL19ZOLbGPP+1UmwPQqASIJYbBXXkk9sc/gld/h6qdmmWScegCUM0oYMCnDnT80IGBnzvw47nKa6cS3Uz86gRcvXo1PvzwQ/Tr1w9qtRqdO3fGq6++ijfeeAOLFy++6v5Wq7XMI8RWW8Urpox8pbKJLXWzExehQmZ+5Q0Nr1TGGAiiWhmxV4JclAkhLD4gX6Fq1AvQR8JxeGlAjhcIuWbAKQuILTVKPtaozA9If30ZhUrFHhvuWUzFhQnI9PF4VU6RMo9ImX3Cfe9zI8kxA04ZZUboxYUBmT4es8g0KfneY3ujgEyT8v+sIqDIDszeLaFzsoC5/VXo0VDE019J2HPWfcx7m4h4q7eIBQ+rMKKdiFVHZby4pvLOWUZBOeV3gY/0NjmV9C6zj4jM4uO1TNbBoBXwzqpcmG0SiqwS3lyZA6cEZFTwkaxAyCiOW9k8K/qOd5HkPZ+XiDcATF5dgFZ1NejezHtnR6dGWhz4w46vD5rhlGRcynPiw42m4nBVbp3pqr8jb976O6dI9n59hwvILPRxfRfKXsoDAZk+HrOzOmS8s8mGe5qpyozkfWejDa3eNKH9u0U4nyfjg4crb2SkK59HlM7n5V3fPvJ5qX3ubWXAW3+LxoKR1TCiWxhW7Tfjxc9yfYZl2Z4i3NtKH9A5pFxpG+4lrXzUPZmFspf6TXTlhYzCK2XDtR/Tm04N1Dhw1omvf7Yr13m+hA+/txZ/R8VuoN15+trraSXe3uJ0Jd7FdX8518nZHOXYH3xvxcjOWvx3gAGRBgGDF5iRa/aM09hlZrR8owBdp5oQrgP+c5//+f6WWipMfsCAOQONmHivAedyZAz82HTNIyrfXW9BQoTg6tg9nyth+QE7pvU3oG1dNepUEzGsow5t6qiw3MdISkA5R97OYaFVme4kEGG9VuXn/eso18JFn+0cX6b108PhBNq/Y0LaGyZMXGPFzP561K1WOVOZuK9Rb/W397i426le4n8d13X7+mpkFkqYu80Mm0NGnlnCu98pN0AZPsrUQAhVO/VsnrLt+zsk9G8hYnY/FZpVFzD0Syd+z2FHINH18mvm1OzsbNSvr6yWEBkZiezsbABAp06dMGrUqKvuP3nyZEyaNMnjvQkD6+O1wSnXFY7Vewsx8bMs19+zRvlYEYIqRJX2MKRT3wM+5g8kCoTVh6yYuMrdgztrYOXPzXYzujLtXbcUAUPbKg3VJgkCDpyX8fkhCbcVPxL9yC3uRmxqvID4cGDoEglncmXUiQ78aKHV+0yY+EW26+9ZTwbmR4vSqoWrMO2xOExakoNFWwsgCsA9rY1oWksTknlkVh8wY+LyAtffsx6LDsr3bDpqwe6TNiwf43sevE6NdHihdzheW16Al77Ih1YFjOoejn2n7RCDPFHK6j2FmPiZ+8erWf9g/V1RdqeMZ4vn+Xutd9l5tIZ10KBfS7XSAbjVhvGrrJj1iA5CENJ69X4zJi5zzxE96/HgTRXzSHv33WVqTQ3iI0UM/V8OzmQ6UKfU4joHfrfh5GUn3vxbcOZGuxF1SlHjhZ46vPa1GS8tB7RqYFQXHfadcf5l59K6Uq8pi0AoI6om99Gj6zQT1h21Y0Ab9+jRl+/U4amuWvyeJeO9TVZM+c6Kib396wjs0tA9eisVwC1JKnSbVoB1R+x4qLX3ORmv+GibFWsO27FwaBh0xR3Qxy874ZSBu2d6zuNmcwLRRmWb1v/nvo7uS9Ng0n2+5zIOVFj/KqZvsaHAIuPjgXrEGARs+NWJ55ZZ8MkQA1KDMJXL6oNWTFxlcv09a1BEOVsHV8PqakzuF4Y31xbhvfVmiAIwqIMeceGOv9x1fS3t1CvbPHKL8hgyADStrsLOPxxY9rOEcV0qd+oeor86vzoB69evj9OnT6NOnTpo3LgxlixZgttuuw2rV69GdHT0Vfd/+eWXMXbsWI/3tNv6XXc47mhhRFqyu6FrK54wOKvAiYQod9QyC5xoUuvmqPDKKMqBLDmAsDiPtwVjHGRTRoUPL0QmQazbEbYVV+/crUzRBkAlyMjynBYIWUVAXNVp299U7mis9VgZzVb8NENWoYSEEqNHMk1ymRXirogxClCJKDMRe2ahjLjwyvlluCJiDIBKQPHkyu5WXKZJ+VXdm7iwsovhZBbJiAtzH1MtAimxnvs3qAb8+KfvX0/Taijb/5ETnE7AO5obkFa3hutvz/Lbnb6ZBU40SfI+T19MmEpJ71Kj1jILJMSVGB3YqbEB6ycYkFPohEoUEGkU0enVc6gdW3krCF5xR1Odxwq+Hvm8xCi4zEIJTRK9hy/GKHrP5yXiveuEHWeynbjtNc964JlFeWhTrwiLnlQ6Bx/rEoahnY24XCAhyiDiz2wn3ltXiNrVgtugviPNiLTkJNffrvTPv3nr7xijUOL6dlPKJx/Xd7hQdnuTjLhSI6/sThnPLbfifJ6M+QP1XufzjDEKiDEKqBcLNIjT4fYZZhz8U0KrAKxUWtodTXVIq+NerMV9fZfO50408TEPZ0yYj3xe6ERcOasap9VRjvdHlrNMJ+DSPWY0SVSjea3Azv3pSttCL2nlo+6JCxfKxs0kufJCfPF+WYUyEiJKbuO7DvTlsXQdhnbQ4nKBsjr0n7kS3ttoRe2YitWL7jwtASiRrleNt7fzdCXeyr9ZplLxLpTRpIbosU1KvPs7tGoBtaMFXMjzPHZ8uIj4cKB+HBBlAB6db8aozlqPdoW/Ig0CkmNF/JFd/sirudutmP2DFfMGhyG1RNoV2ZQ6f+mTYVCV6ow3Fhd7K0a6fxAN112Jk/dzGK6DzxGu1xrW61V+3r+Ocq1Q8tnO8eZMtoTFe+1Y/aQBDROUc9q4hgo/nnXi0312TLonCOVaEy3SapdspxaXa6XbqYUSmtS8WjvVS/yvs5163y063HeLDpmFEgwaQVkAbbslqPV3qNqpCcXbltkmVsCFAhDRdfKrBnzsscdw6NAhAMD48ePxwQcfQK/X47nnnsMLL7xw1f11Oh0iIyM9Xjrt9QclXC+ibrzG9UqpoUF8pAo7f3XP81RolvDT71a0TK7cFT4rjWSHfPEwVHU7lnhTgCo5HdKf+yt8eFWLh4CiLEgnN1X4WIGkVQFNE4BdZ93vSTKw+xxwSw3f+9GNK1wnoG6syvVKSRARHy54TKBdaJHx0zkHWvqYKFmrFtAsUeWxjyTJ2HXK7nOfG4lWJaBZdWDnGXfjUJJl7Dojo2Wi98ZVy0QBO//wbEzu+MO9vVYloHkN4HSpxyV+zwESI303uI8V9x0l+GjEV5T38lvEzuMlym+LhJ/+sKJlPe/lt1YtoFltrcc+kiRj13ELWiaX7TiKCVch0ihi13ELsgol3NH82kZUBFK4TkTdOLXrlVJdhfgIETtPuOf/K7RI+OmsHS3reO/80qoFNEtSe+wjSTJ2nbChZXEHyBN3GPHVs7FYMcb9AoDx90Vgcv8oj+MJgoDqkSroNQK+OWRBzSgRTZOCe72E60XUTdC4Xik1y6m/faT/X41WJaBZTRE7T7s7rSVZxq7fnWiZ5L0N1LKWiJ2/e3Zy7zjtRMta7u2vdAD+kS3h40f1iDFe/Zq9MqrCFqSpo8L1pfO52ns+P2NHy7reO+SUfK7xns997AMAx/5UIlW6k8dklbD2Jwv63Rb4616pe0TsPF267nGgpY9O1pa1VR55AQB2nHS6tq8VIyh14OnSdaDT5zHLo1znonKd/2xHzUgBTWtWrCPMZ54ulUdLallL5REnANhR4jzVir4Sb/cxC60yfvrTHe/miSpoVcDpLHeHlt0p4888GYlRvvO/K98HaIYBk1XG2WzJ1SnpzZwfrPjvVitmDzSiRZJnujWpIcIpA9kmGXVjRY9XfHH+LfnelcdOlbxT6hye9J3XrjWs/nDlgd+95QEfeb+Wl7xfTp7x5sqcxaVHrYuCO50DrWw7VaXk1ZPuR7evvZ3q3qei7dS4cBFhOgFrf7ZBp4bHXNqBFqp2alIUkBAOnM4uvY2MxLLrntE1EgW+qiq/SonnnnvO9f8ePXrg2LFj+PHHH5GSkoK0tLSABe56CYKAwXdEYta6PCTHa5AUq8aMb3KQEKVGj1vcw8OGzriIHrcYMbCrUmqYrBLOZLgL43NZDvxyzoooowqJ1ZRTlGty4kKOA5fzlErr9CWl8o2LVCE+MrSdC469c6C5511IF3+CdOEQ1G0fBzRGOH5W5vDT3PMu5IKLcGx9W9lB1ECIUx69FkQNhPAaEBKaALYiyLl/lDiyAFWL/nAcXgbIN96cTENaAv/cADRLUFb+XXQIMDuAB5son7+8Xvnl6Ll05W+bEzhZ/PSh3QlcNgG/ZABGDVA3WnnfZAPO5Lm/41y+sk2UHkgM3ah/v+mMYYiv617qOK5WMmo1SYMpNwc5F86Ws2foCYKAwR30mLXFguRqIpJiVJix0YyECBE9mrhvAId+XIAeTTQY2F55vGdouh7jl5vQPEmFtCQ1Fuy0wGwD+pZ4/CWjQEJmoYQzxTcQxy85EaaTUDNKRLRRaYSez5WQZ5ZwIU+CU5LxywXlmq9TTYWwIK6aO7StiPFrJTSvLiGtpoAFP0ow24G+zZXvfGmNEwnhcD36MKi1iMFfODFvr4Tb6wv45piEIxeB13u6G9PDbhUxdrWEtrUktKstYNtpGZtPylj4iHKMM7kyvv5FRpd6AqINwPEMGZM3S2hbS3k0uDIIgoDBXSMx67s8JMerlfJ7TR4SolTo0aJE+f3+JfRIM2JgF+WCHHp7BMYvzkLzOlqk1dFhwfcFMNsk9G3nHj2xbFchGtTQoFq4iIOnbfi/5TkY0jUC9au789H5bAfyiiRcyFHmGfzlnNLxUCdejTBd8EaRCoKAwZ2MmLXJhOQ4lZLPvzMhIVKFHs3cnV9DP8pBj+Y6DExXzsXQzmEYvyQPzWtpkFZLgwU/FMFsl9G3rXIdxEeoEO+lzEqMFlGrxCiBud+b0KmRFqIgYP1hC2ZvMWHqo1FQVXLLyF1/5yI5QY2kWA1mfJ2jpH/J+nv6BfS4JQwDby+uvy1e6u+zVkSFlaq/s0vU35eV7eMiVYiPqtz6e2g7DcavsqJ5TRFpSSos2G1X0u0WJS++9JUVCRECxnVTyqtBt2oweJEF83bZcXuKCt8cceDIeQmvFz/ua3fKGLPMiqMXJMwaoINTll1zqkUZBGhVAg796cTP5yW0qS0iUi/gbI6M6d/bUCdGQKvruOmuCEEQMLizEbM2Fir5vJoKM74tLM7n7kczh/4vW8nnHZVhH0O7GDH+i+J8XluDBdtMMNtk9L1V6cg7k+nA1wcs6NJEh2ijgOMXHJi8qgBt62uQWmqE4dpDFjidMvq0Dk7n/9AOOoxfYUbzRJWStrtsStq2Kk7b5WYkRAoY10OJ76B2WgyeX4R5O6y4vaEa3xy248h5J14vnrNOEAQMbq/FrK3W4jpQxIxNSv7o0didb5W6SsaFPGVutl8uKPm8TjXRVVfN3W5FpxQ1RAFY/4sDs3+wYWp/Q0Cu86EdtBi/0qLEO1F05+mWxfFeqdTb47rr3PFeUIR5O224vaEK3xwuztP3loh3Ow1mbSuOd7SAGVtsHvEO1wkY0FaDmVtsqBEpIjFKxLydSpl9V1Ple7//zYFMk4wWiSKMWgEnLkt4e4MVrWurUCvav3z/5rcW3JGqRmKUiMsFEt7fYoUoCri3hfc0nv2DFTM2W/FOPwOSokXXXG1GrYAwnYB6cSrc10KDl1aY8VIvZXXobJOMnacdSK0u4vZG3ju7B7TVYvEeG97+zoJ+rTTYddqJdUccmPWou6y8WlgDaWh7DcZ/VVyuJaqwYE9x3r9FSa+XVipzIbrywG0aDF5oLs4DanxzxK7kgXvcdV6uWcaFPAmXi+fWu9LhGxcuID5cRP04EXWrCZi4xoIXe+gQbRCw4VcHdpxyYtaAypnvVBAEDE4vbqfGqpRr1NVOdbc5h87LR4+mWnc7taMe45eZ0DxRjbRaaizYUdxObeOOv6udmq1cz97aqZ/ssqBVHTWMWgE7Ttjx9rdFGNvLiEhDcMv1ULRTBUHAsFtFzNwuITVeQpMEASuPSDiVDUzvc+M/6UN0owlI67du3bqoW7duIA5VYcN7RMJslTDhs0zkmyW0aaDH7H9Uh07jLiDOZNqRU+ju1Dr8hxVDZlxy/T1leQ4A4IF2YZgySJmnatPPRfjnJ+75B8d+rAyTGX13FJ6+J3hz3VwL57FvAGMs1J3GQgiLg3z5F1iXDAWKlPmWhMhEQHb/WiqEJ0D/2BrX35p2I6BpNwLOM7tg++xvrvfF5E4Qo5Jgu4FWBS7p7oZAthl4f48yDL1xPPC/+9yPA18oAEr+QJhhAh76wv3GxweU162JMub3Vd47chl4bKV7m7d+UP5/f2MZb/QIepQCrm7zNhj3yXrX3w//U+kI3rF8IRaMfyJUwbpmwzvrYLbLmLCqCPkWGW3qqDF7cLhrPh1AeSQkp8j9y2DvFlpkmyTM3GhBRvEjGbMHh3s8ZvH5Xis+2OwecTRwrjIfzxsPGtG3tdIIm7HJjJUH3CNQHvxQed5gwePhaFcv8I1oV/gbi8guAmZul5BRBDSJB2Y/pHI9ZnE+X/aYw6t1koB37hEx7QcJU38AkqOB9x8Q0ahE513PhiJe6wl8tFvC/20C6sUAM+4X0aaWso1GBHb8IWHBj8rqhTUjgF6NBIxqX7kNq+HdI2C2SZjwRbZSftfXYfbIBM/0znIgx+Quv3u3DkN2oYSZa/KQka88Ojp7ZALiSjxy+PtlB6Z+nYu8IgmJ1dQY2SsSQ2/37CGbsTYPK/e45/p58G1lmfEFTyWgXcPg3lAM72qE2SZjwrIC5FsktEnWYvbj0aXyuQM5Jne+632LXsnn3xUio0B5dHj24zEej0Ffi62/WjFrkwk2h4zGNTX4YHA0ujQOzci74T2jlPPwaVZx/a3D7NE1StXfnul/+IwVQ6a7l4Sfskz5peeBduGYMri4/v6pCP/8xD3/4Nh5xfV37+hKr797N1Mju0jGzO/tyDDZ0KS6iNl/07semzufJ0EQ3PFtXVuFdx7QYdoWG6ZutiG5moD3H9ahUYKyzaUCGZuOK+fjgdmeq10vGKhHu2RlhOf6Yw7M3CrBbFMeJezcQIVRnTQeq2YH2/Dbw5T0XZrvzufDY7xc3yXyeUuDks+/LSjO5xrMHu7O5xq1gB0nrFjwg9I5WDNahV4t9BjVI6zM9y/dY0bPFvqg3SD3bq5BtknGzM1WZBQ/ujp7oNFV95RJ2zpqvNPPgGmbrJi6Uenwen+AAY1KzGU2vKNWOWerLcV1oAqzBxo9ztmMzVasPOTuCH/wf0o5tmCIEe2KV+Hd+psDs7ZaYXMCjaur8MHfDB5zxlUo3s2K472lON7VRcz+e8l4yxAEdxu0dW0V3umrx7TNVkzdJCvxfsSARgkl4p2uhdkGTPi6RLwfNXqscvxCDx1UgtLBZLHLuCVJhfmDDK6Vd3Vq4Mv9Nkz5VoLNCdSIFNCrsQZPdPJ/eoFL+RLGLVUWH6lmFNCmjgpfDA9DtTDvafzZXhvsTmDMEs+5a0Z31eLpO5R65Y0H9PjvVive/M6Cy/kyoo0CWtZS4fZGvm/RasWImPV3I6Z8a8HC3TbUiBTw7z56dE5x73O1sAZS72aa4nLNViIPGNx5IN9LufZgcR7YbFPywMN6jzyw6bgD/1zlXhl+7HLl/6O7aPB0Vx00KgH/G2DAu5usGPWFBUU2GXViREy5X4euDSvvx53hnfXKNfqVyd1OHRJRtp1aYjGn3i10yjWz0exupw6J8Gyn7rF4tlPnKG3QN/qGudqpP59zYOZGM4psMurHqzCpTxjubxX8+jsU7VQAGNJGhNUBTNkiIc8MpCYA8x5SBWXKGqKbnSDL8nUPmn7mmWeQkpKCZ555xuP9999/HydOnMC0adOuOyDy+juve5+bgWX/8VAHISQ0xuB1otzIRs/84+ob3YRm/adVqIMQGnlVdKKS2omhDkFoWKxX3+ZmFF5FJ2K9UEUXy4r6Cw6LDwRT0dW3uRkF69nKG11VfU7MGbyVZW9ouptj7tnrlpt/9W1uQsITR0IdhBvChp43/lRJwdZjfZDmRbnB+fVz0LJly9CxY8cy76enp2Pp0qUVDhQREREREREREREFjl/dv1lZWYiKiirzfmRkJDIzM73sQUREREREREREoSZU0QHP5OdIwJSUFKxbt67M+2vXrkX9+vUrHCgiIiIiIiIiIiIKHL9GAo4dOxZPPfUUMjIy0K1bNwDAxo0b8e677/o1HyAREREREREREREFj1+dgI8//jisViv+7//+D//+978BAMnJyfjvf/+LwYMHBzSAREREREREREREVDF+LwkzatQojBo1ChkZGTAYDAgPDw9kuIiIiIiIiIiIiChAKrwudHx8fCDCQUREREREREREQebX4hB0U/CrE7BevXoQyllO5tSpU34HiIiIiIiIiIiIiALLr07AZ5991uNvu92OAwcOYN26dXjhhRcCES4iIiIiIiIiIiIKEL86AceMGeP1/Q8++AD79u2rUICIiIiIiIiIiIgosAL6KPjdd9+NZcuWBfKQREREREREREREVEEVXhikpKVLl6JatWqBPCQREREREREREQWIKMihDgKFiF+dgK1atfJYGESWZVy8eBEZGRn48MMPAxY4IiIiIiIiIiIiqji/OgEfeOABj79FUUR8fDxuv/12NG7cOBDhIiIiIiIiIiIiogDxqxNw4sSJgQ4HERERERERERERBck1dwLm5+df80EjIyP9CgwREREREREREREF3jV3AkZHR3vMA1gep9Ppd4CIiIiIiIiIiCg4rrFrh25C19wJuHnzZtf/f//9d4wfPx5Dhw5Fhw4dAAA7d+7EggULMHny5MCHkoiIiIiIiIiIiPx2zZ2AXbt2df3/9ddfx3vvvYe//e1vrvf69OmDFi1a4KOPPsKQIUMCG0oiIiIiIiIiIiLym+jPTjt37kTbtm3LvN+2bVvs2bOnwoEiIiIiIiIiIiKiwPGrE7B27dqYPXt2mffnzJmD2rVrVzhQREREREREREREFDjX/DhwSVOnTkW/fv2wdu1atGvXDgCwZ88eHD9+HMuXLw9oAImIiIiIiIiIKDBELgxSZfk1ErB379747bff0KdPH2RnZyM7Oxv33XcffvvtN/Tu3TvQYSQiIiIiIiIiIqIK8GskIACcPn0av//+Oy5cuIClS5ciKSkJixYtQr169dCpU6dAhpGIiIiIiIiIiIgqwK+RgMuWLcOdd94Jo9GIAwcOwGq1AgDy8vLwxhtvBDSAREREREREREREVDF+dQL+5z//waxZszB79mxoNBrX+x07dsT+/fsDFjgiIiIiIiIiIiKqOL8eB/7111/RpUuXMu9HRUUhNze3omEiIiIiIiIiIqIg4LogVZdfIwFr1KiBEydOlHn/hx9+QP369SscKCIiIiIiIiIiIgocvzoBn3jiCYwZMwa7d++GIAg4f/48Fi9ejOeffx6jRo0KdBiJiIiIiIiIiIioAvx6HHj8+PGQJAndu3dHUVERunTpAp1Oh+effx5PP/10oMNIREREREREREREFeBXJ6AgCHjllVfwwgsv4MSJEygsLETTpk0RHh4e6PARERERERERERFRBfnVCXiFVqtF06ZNAxUWIiIiIiIiIiIKIpErg1RZfs0JSERERERERERERH8dFRoJGFC5+aEOQUjo6yaEOghUiWb9p1qogxASI189EOoghMSsdzuEOgihUUXLc+QVhDoEoVFoCHUIQkOsor+jFphCHYLQ0Nw4TeZK5bSHOgShodGGOgShoZZDHYLQMBWFOgShER0Z6hAQUQhU0RYsERERERERERFR1cFOQCIiIiIiIiIioptcFX22gYiIiIiIiIio6uHCIFUXRwISERERERERERHd5NgJSEREREREREREdJNjJyAREREREREREdFNjp2ARERERERERERENzl2AhIREREREREREd3kuDowEREREREREVEVIXB14CqLIwGJiIiIiIiIiIhucuwEJCIiIiIiIiIiusmxE5CIiIiIiIiIiOgmx05AIiIiIiIiIiKimxwXBiEiIiIiIiIiqiI4GqzqYtoTERERERERERHd5NgJSEREREREREREdJPzqxOwW7duyM3NLfN+fn4+unXrVtEwERERERERERERUQD51Qm4ZcsW2Gy2Mu9bLBZs27atwoEiIiIiIiIiIiKiwLmuhUF++ukn1/+PHj2Kixcvuv52Op1Yt24dkpKSAhc6IiIiIiIiIiIKGEEIdQgoVK6rE7Bly5YQBAGCIHh97NdgMGDmzJkBCxwRERERERERERFV3HV1Ap4+fRqyLKN+/frYs2cP4uPjXZ9ptVokJCRApVIFPJBERERERERERETkv+vqBKxbty4AQJKkoASGiIiIiIiIiIiIAu+aOwFXrVp1zQft06ePX4EhIiIiIiIiIiKiwLvmTsAHHnjgmrYTBAFOp9Pf8BARERERERERUZAIXBmkyrrmTkA+AkxERERERERERPTXJIY6AERERERERERERBRc17UwyBWvv/56uZ9PmDDBr8AQERERERERERFR4PnVCbhixQqPv+12O06fPg21Wo0GDRqwE5CIiIiIiIiIiOgG4lcn4IEDB8q8l5+fj6FDh+LBBx+scKCIiIiIiIiIiCjwBE4MV2UFLOkjIyMxadIk/Otf/wrUIYmIiIiIiIiIiCgAAtr/m5eXh7y8vEAekoiIiIiIiIiIiCrIr8eBZ8yY4fG3LMu4cOECFi1ahLvvvjsgASMiIiIiIiIiIqLA8KsTcOrUqR5/i6KI+Ph4DBkyBC+//HJAAnatZFnGzI1mfLnPinyLjNZ11JjYJwzJcapy91u8y4K5P1iQWSihcQ0VXr03DGm13Kfji70WfH3IhqMXHDBZgT2vRCPS4Dlw8nSmE2+vK8L+Mw7YnTJSq6vxTA8D2tfXBCeemy348kebO573GpAce5V47rZi7g4LMgtlNK6uwqu9DR7xtNplvPmtGd8ctsPulNGxgQYT7zUgLtwzrssPWDF/pxW/Z0kI1wm4q6kGE+41uj5fe9iG/22z4PcsCdWMAh69TYdhnfSBPQkI3XlYfsCKf640ez329hciERse3EkVZFnGzE2WUvnceA3xtmDuD1Z3Pr/HWCqfW/H1TyXy+T+jyuTzWVvM2HLcjmMXndCoBOx9JToYUQyohm07odfwsajTrBWiqyfiw3/0x6ENq0IdrGsmyzJmfluIL3cXId8soXU9LSb2jUJyfPlF9uLtJszdYkJmgRONa2rw6oORSKuj9djmwO82TFtbgJ/O2CGKQJNEDeaMqAa9RsDuE1YMmZXt9dhfPhOLFqWOFWg3ejkXLIsPSpi7T0amCWgcD7x6h4i0moLP7dcdlzF9u4Q/84G60cDznUV0re/evvF7Tq/7vdBZwLBbRew+K2PIl5LXbb78u4gWNXx/dyAt3mvD3J324nQT8epdOqQl+U7rdUcdmL7Fij9zZdStJuL57lp0behO5+9+ceDz/XYcueBEnhlY8YQBTWp4Hm/QwiLs/cMz7o+0VmPSPYGvr4DiOO6wueN4t/4qcbRj+mYb/syVUDdWxPPddR5xlGUZM7fY8OUBu3KN1FZhYm89kmPdefnIBSfe3WDFz+edEEWgVxMNxvfSIUxbNl1zimQ88D8TLhXI2PNiOCL1wUv7QJ+L736x4/MfS6T3CGOZ9P7iRxu+PuzA0QtOmGwIehyvVSjr9MpS2df3uVwJPWYWeT32tH563NXUr1uegHDdq+y1KOldV3Pt9yrbzMXprcar9xqRVtt9jzFhZSF2nrTjcr4Eo1ZAqzpqPH9XGOrHl3/cYKgq92Nlwv+jA3N3O5BZCDROEPBqLw3SEn1fc+t+cWL6Vgf+zJNRt5qA529Xo2uK+xzN3GbHmqMSLhbI0KiAZjVEPNtFjVuS3Mfs9qEF50s9eDf2djVGdKi8PB6M9M4tkjBzkxnbT9hxIVdCtTAR3ZtoMKaHARF6Jf7HLjjw0VYL9v/hQE6RhKQYEQNu1WNwenDqcKKblV8tg9OnT3u8Tp48iV27duGNN95AREREoMNYrjnbLFi0y4rX7g/DkpGRMGgFDF9QAKtd9rnPmp+tmLK2CKPvMGD5P6KQWkON4fMLkFXovjGw2IHODTV4sovB53FGLiqAUwIWPB6BZaOi0LimCqMWFSCjwPvNVUXM+cGKRbuteO0+I5Y8EQGDBhi+yFR+PA/bMOVbM0bfrsfyJyOQWkOF4YtMHvGcvM6MzcftmP6wEQsfC8flAglPf27yOM7HOyyYttGCJzrp8fXoCHw8OBydUtwV69bf7HhhWREGtNVh9T8iMOFeI+bvsuKT3dab5jz0bq7FtucjPV6dUtS4NVkV9A5AAJizzark8z5GLHkyojifF14ln9swZa0Zo+/QY/moSCXeCwpL5XP5qvnc5gTuaq7FgFt1AY1TMGmNYTh37Cd89vqYUAfFL3M2m7DoBxNe6xeFJc/EKek9O7v89D5oxpRV+RjdMxzLn41DaqIaw2dnI6vA3SF04HcbnpiTjY6pOiwZE4svx8Th0Y5GiMX3xK2Stdg2IcHj1b+dAbWqqdC8dvAb0zdyORcsa36VMOV7GaPbC1g+UERqvIDhyyVkFXmP8/7zMsZ9I+Gh5gJWDBTRI0XAU6skHM90b7/tSdHj9X+9BAgAejVUErpVYtlt+jcXUCsKaF496FEGAKw5YseU9TaM7qLF8ieMSK0uYvinZmSZvNef+886MW65BQ+11GDFE0b0SFXhqSUWHL/szt9mu4w2tVV4vnv5ZVX/Vmpse87oer3QIzhl25ojdkz5zorRXXVYPsKo5M3FReXHcZkFD7XSYMUII3qkqvHUF2aPOM7ZYcOiPTa8do8OS4YZYdAIGL64CFaHkv6XCiQ8vqgIdaqJ+GJYGOb83YgTl514+SuL1+98dbUFqdWDX4cF41yY7bhqelvsQOcGKjzZKbg/YFyvUNbplSEU13fNSMHjut72nBFPd9XCqAU6p1R+p1hJc7ZZsGinBa/dH44lo6KUum1+fvnp/ZMVU9aYMLqbActHRynpXepepVmiGm/0Dcc3z0ZjztBIyACGfZwPp+T7uMFSVe7HPMJ/1IkpGx0Y3UmN5Y9rlXz+hQ1ZJh/19zkJ476y46FbVFjxuBY9Gop4apkdxzPc4UyuJuJfvdRYNUyLxQO1SIoSMOwLG7JLtQme6azGtqd1rtfANpWbx4OR3pcLJFzOl/DiXUasfjoKk/uGYdtvdryywt1eO3LeidhwAW/1D8PXz0RhZFcD3ltfhE92ea/jqHyCIFT5V1X1l14TRpZlLNxhwcjb9ejeRIvUGmq8+VAYLhdI2PCLzed+87db0L+tDv3a6JCSoMKkPkboNcCyH92dVkPS9RjR1YBbanv/VSXHJOGPLAlPdNEjtYYayXEqjO1lhNkO/HbJ+yiMCsVzlxUju+jRvbEGqTVUeLNvcTyP2X3Hc4cV/dto0a9VcTzvNSjxPKCcmwKLjGUHbHjpTuXXsuaJakx+wIgDZ504eNYBAMgzS5i+yYI3+xpxX5oWdaqpkFpDhW6N3TfHXx2yoXtjDQbcqkPtairc3kiDEZ30mPODBbIcuIZIKM+DXiMgPkJ0vVQisPu0Aw+1Dn7HmCzLWLjTgpFdS+TzflfyeXnxLs7nrYvjfV9xPt/vvjaGpOsxooset9T23Xh4prsBQ9P1aFQ9tI3o63Fk67f4atprOLj+rzP67wpZlrFwmwkje4Sje3M9UhM1eHNANC7nO7HhsO9GzvzvTejfzoh+txmRUkODSf2ioNcIWLbXPYJ1yqp8DOoUhhHdwtGwhgb1E9S4u6UBWrVSCWrVAuIjVa5XdJiIjYet6HurIegV5Y1ezgXL/B9l9G8uoF9zESmxAib1EKBXA8sOey87F+2X0SkZGHariAaxAsZ0FNE0AVh80L19fJjg8dp0Uka72kDt6OJ0Vnl+Hq0HNp6U0bdZ5TWI5u+yo38rDfq11CAlXsSke3RKfj3o8Lr9oj12dEpRYVi6Fg3iRYy5Q4emNUUs3uvOG/enaTC6ixYd6pVfVhk0AuLDRdcrXBecOM/faUP/1lfiqHLH8YD3/Lxot61EHFVl4ijLMhbutmNkZx26p2qQWl2FNx/Q43KBjA3HlPO25bgDapWACb11qB8nokWSCq/do8d3vzjwR7bnDfFn+5QRt493CH4HWaDPBVCc3l116FDf9+iXIe21GNFJh1tq3Tj1V6jr9MoQiutbJXpe1/HhIjYcc+Dupmqvo2AriyzLWLjdjJG3G9C9aXF69w+/jnsVPVIS1Jh0f1iZe5VHbtPj1noa1IpRoVmSGs/2NOJCnoQ/c4Lb+VVaVbkfKxP+PQ70v0WFfmlqpMSJmHSXWqm/f/L+vYv2OdCpvohh7dVoECdiTFcNmtYQsPhH9/b3NVMhvZ4KtWNENIwXMb67GoVW4NfLnmkapgXiwwXXy1iJeTxY6d2ouhoz/x6Bbo21qBOrQvsGGjzX04jNx+xwOJU2Tr82OrxyTxhuq6dB7Woq9GmpQ9/WOqw/4vt7iagsvzsB9+3bhxdffBEDBgxA3759PV6V5VyOhIxCGekN3DdqEXoRabXUrpu70mwOGUfOOz32EUUBHRpofO7jTbRRQL04EV8dsKHIJsPhlPHFXgtiwwQ0K+dxB3+44lmioRuhF5CWpCo/nhecHvuIooAO9d3n5sh5B+xOeGxTP16FxCjBtc2Okw5IMnApX0bvmfno+m4enl1iwoU8qcR3AbpSdbNeA1zMl/FnbuAaIqE8D6WtPGiDXgPc2TT4nQTufF4q3teSz0vH+zrzOVW+c9lOZBRISG/o7mCOMIhIq6PFwT+8N3JsDhlH/rQjvZF7H1EU0KGhzrVPVoETh87YUS1cxICZmej42iUM/DALP5723XDadMSC3CIJfW8N/iOxN3o5Fww2p4wjl4D0uu7GuygI6FBXwMEL3jsBD16QPbYHgI7JAg6e9759pknG96eBfs193yBsOgnkWoC+zSrnJsLmlHHkgoT0EjfzoiCgQz0VDp7zfvN08JzTY3sA6FhfhYPnrj+NVh+2o/07hbhvVhHe3WiFuZyRC/4qP47ew6zE0bMy7dhA7Ton53Ll4mvEfUzXNVK8jc0JaFTKd12h1yj///GM+9yeyHDiw602vPmAHsHu9w3Gufgru9nr9FBf31ccvuDEL5ck9GsZ/HZaecq9VzlTXno7kJ7i7qAXRQEdUrQ4eMZ7R3GRTcbyH62oFSOiRlTljvGoKvdjJdmcMo5clJFez32uRUFAh2QRB//0Ua79KSE92TNtOtbzvb3NKeOLg05E6IDGCZ77zd7lQLupFjw4z4q5uxxwVOLoz8pM7wKLMj2LWuW7oiqwyIgyVt0RXUT+8KuW+Pzzz5Geno5ffvkFK1asgN1ux5EjR7Bp0yZERUVddX+r1Yr8/HyPl9V+/RV9RvHw4dKPY8aFi8gs8F4Y5hTJcEpAbLhQdp/Caw+DIAj4+LFIHL3gQJt/5+CWSTmYv92C2UMiEBXg+VcyCpW4eI1n4dXi6XufjEJlvonSc2vEltjmbI4EWQb+t82Cl+82YPrDYcgzy3h8YSFsxY8fdUpRY/0vduw8ZYckyTid6cTHO6weYQ+EUJ6H0pYdsOHeFlrXzVUw+Yx3mOAzz/qOt+996MZw5fGV2Ahv5ZqP9DZJ3tM7QkRmvrLP2Wzl5uv97wrQv50Rs5+ohmZJGgydlYXfM7w3wJbtMaNTqg41ooM/quRGL+eCIccMOGUgtlQfa5wRyDR53yfT5GN771NhYeVRGWEa96PA3iw7LKFTXaBGROU0onOKZCXepevhMMFnWmcWyogN81Jv+3jsypd7m2vw1gN6LBhkwIiOGqz62YEXVwT+MSJXHMOuvdzOLJTLPSeua6TMeXBv0z5ZhcxCGXN32GBzysgzy3h345X6WPlem0PGuOUWvNBDh8RK6CwIxrn4K7vZ6/RQXt8lLTtgR4M4Aa1DPCrSVad7raeult6+r/UrPt1lQetJWWg9KRtbj9sw77FI1+j+ylJV7sdKyim6Un9fTz73Un572X7zb060fseCW96yYsEeB+YN0CKmxPcMaqvGu/drsPBRLR5pqcL/djrw9qbK+zGgstI7xyThv5steLic6Yj2n7Fj7c82PNyWcwISXQ+/ZhB94403MHXqVIwePRoRERGYPn066tWrhyeffBI1a9a86v6TJ0/GpEmTPN6b8FASXnu4drn7rT5oxcRV7jujWYMqd/7BkmRZxuurTYgNF7F4uBE6jYCl+6wY9UkBvhwVhYQI/yue1T/ZMHG1+45u1qPhgQiyXyQZsDuBV+42uObHevchIzq9nY/dvzvQOUWDh9tocTZbwsjFJjgkIFwnYFA7Hd7fYnHNNeaPG+k8lHTgrAMnMyS82Tc4j1CtPmTFxFUl4j3wxog3Bcfq/WZMXOqe4XnWsJigfM+VH4kfaa88MgwATZM02HnCimV7izCud6TH9hdznfjhVyumDooOSnhupOv7Wsq5v6plh2Xc20SAzsdN4cUCGT/8AUy95y89O8g1e6S1Oy1Tq6sQHy5g6CcWnMmWUKfaX/8cNExQYfL9erz5nRXvbbRCFIFBt2kQFya46uN3N1rRIE5En7S/br7+K2GdXvksdhlfH3ZgVOfKnwty9UErJn5V6Pp71uDIcrauuPtaapGeokFGgYR5P5jx7OcF+GxEFHRB/JG6qtyPhUq7uiJWPK5Fjhn48qATz660Y8kQrasD8bHb3LfvqQkiNCpg4joHxt2uDkoHcCjSu9Ai48lFBWiQoMJT3bzPB3n8kgOjPynE6DsM6NSQ9RnR9fCrE/DkyZO45557AABarRYmkwmCIOC5555Dt27dynTwlfbyyy9j7NixHu9pv+551e+9o4kWaSXmhLgyQiOrUPIo5DMLJTSp6f2XvxijAJUIZJX61SWzUCqzUmR5dp1yYMuvdux5JQbhxSvNNeujxo6Tdqzcb8WIrv5PynxHqgZpSe4C1lb89ITXeNa4Wjw9f11R4qmENz5cgN0J5Jslj1EyWSW3KR4ZklJipbFqYSJijAIuFD/qKwgCnu9lwHM99MgslBFjFLDrtPKLVO0Y/yvfG+k8lLT0Rxua1FCheWJwVuG6o7HWY2U0m8MdHo94m2Q/4i1fVz6n4LujqQ5pY+Ncf7vKtQIJCZHu9M0slNDER56LCRO9p3eBhLhIJb2v5J2U6p7HaJCgxoWcso9oLd9bhGijiG7NgvPr6o10fV9LORcMMQZAJQBZpUbxZRYBcWHe94kL87G9lye2952TcToHmHqv75uC5UdkROuBbg2uM/AVEGMUlHiXrodNstcyF1BGwJSebD2zUEJcWMVueK6sVvpHTmA7AV1xNEkASlzHJt9lcFy4UO45iS/+N8skI6HEPVhmoYwmNdzHvK+FBve10CCzUIJBqywKM3+X3VUf7/7dieOXJXx7tAAAcOUbO7xdiCc7a/HM7YGd6zYY5+KvpKrV6TfC9f3tLw5Y7MADIejoVu5Vol1/e9yrRJa+V/FRp/u8Vyl7DiP0IiL0QHKcCrfUVqPdf7Kx/qgN994SvDmrq8r9WHlijFfq7+vJ5yibz71sb9QKqFtNQF0ALZNE3DnLiqWHnHgy3Xt+SUsU4ZCAc3ky6scGvoys7PQutMoYvqAAYVoB7/89HBovjwKfuOzEY/MK8PCtOoy6I7QLIf2VVeF1Mao8v1oOMTExKChQGo9JSUk4fPgwACA3NxdFRT6eSSpBp9MhMjLS46XTXD0o4ToBdWNVrldKgvIr/s6T7vkxCi0yfjrnQEsfE8hq1QKaJaqw85R7H0mSseuU3ec+3lyZQ6j0xSMI7hE3/ioTz3hRiecp91DvQouMn/50lh/PmiqPfSRJxq7T7nPTLFENjQrYedq9zalMJ87nya5tWhf/ezrL3UmQWyQhp0hGYrRnmqlEAdUjRWjVAr752YaWtVWoFuZ/4/RGOg9XmKwy1h6xoV/r4P26XDaf+4j3NeXzUvG+znxOwReuF1E3Tu16pVRXIz5CxM7f3BNjF1ok/HTGhpZ1vec7rVpAsySNxz6SJGPXCatrn6RqKiREijid4dnh93uGA4kxno02WZaxfK8Z97c1eG18BcKNdH1fTzkXSFqVgGbVgZ1n3JWGJMvYdUZGy5rez3vLmoLH9gCw4w8ZLRO9/GBxWEaz6kDjeO/HkmUZy4/IuL+pELR09karEtCspoidv7vPtyTL2HXaiZY+FnBoWUuFnac98+6O0060rFWx9Dl2SelUSQhw55Irjqe9xdF7mJU4ej7SteOUw3VOakUrE8CXPGahtfga8XLe4sJFhGkFrD3igE7tnhdzRn8DVj5pxIri17/vUzr6PxlqxKO3Br7TJBjn4q+kqtXpN8L1vfSgHXc0UqFaBX8k8IfPe5VTJe9VJCW965SX3mqP+xtJkrHrpB0t65R/jcpQ5pILpqpyP1YerUpAsxoCdv7u7piXZBm7/pDQMslHuZYkYucfnh35O373vb37uOWn6bFLMkSh7KPJgVKZ6V1okTFsfj40KuDDgRFeR7T+dsmBIXPz8UArHZ7rGfw5q4lK++CDD5CcnAy9Xo927dphz549PredP39+mRWJ9XrPARayLGPChAmoWbMmDAYDevTogd9++y2ocfCr5dClSxesX78eLVq0QP/+/TFmzBhs2rQJ69evR/fu3QMdRp8EQcDgdD1mbbEgOVaFpBgRMzaakRAhokcT983y0Hn56NFUi4HtlRM+tKMe45eZ0DxRjbRaaizYYYHZBvRt4/7VLKNAQmahhDPF82gdv+REmE5CzSgR0UYRrWqrEWkQMH6ZMgxZpxHw5T4r/syRcHtqYBvRgiBgcHsdZm21uuO5qTieJVavHDq/ED2aaDCwnRKPoek6jF9RhOZJaqQlqbBgp1WJZyvl3EToBfRrpcWb68yIMggI1wn4zxozWtZWuQrkenEqdG+sxhtrzZh0n7LNexvMqB8nol3xpN05JgnfHrXjtmQ1rA4Zyw/YsO6IHYseC+wjL6E8D1esPWyDU0KlPkYlCAIGdyjO59VEJMWoSuTzEvH+uECJ95V8nq7H+OUmNE9SIS1JjQU7i/N5iQ5MVz7PUholpfM5AJzPlZBnlnAhT4JTkvHLBeUmpE41FcKCtKpmRemMYYiv6x7aFFcrGbWapMGUm4OcC2dDGLKrEwQBgzuHYdbGQiTHq5FUTYUZ6wqQEKlCj+buSmPorCz0aK7HwE7KkLGhXcMw/vNcNK+lQVodDRZsK4LZJqPvrQbXcYfdHo6Z3xUgtaYaTZI0WLnPjFOXHZg+2PMR5F0nbDiX7UT/dpXXuLrRy7lgGdpGwPh1MppXl5BWQ8CC/TLMdvciHS+tlZAQDozrrFyPg1oLGLxExrx9Em6vL+CbY8riIq/39LwWC60yvj0u46Wuvq/RXWeBc3lA/xaVfx0Pba/B+K+saF5TRFqiCgv22GC2y+h7i3K+X1ppQUKEgHHdlXQedJsGgxeaMW+nDbc3VOObI3YcOS/h9Xvc9XauWcaFPAmXi+ckOl1crsWFK6uGnsmW8PVhB7o0VCHaIOD4JQmT11vRto6I1CCsfj60gxbjV1rQPFGFtEQRC3bblTgWL1Tw0kolf7vi2E6LwQuKiuOowjeHHUoc71Wue0EQMLidBrO2WZW6IFrAjC02JEQI6NHYnU8/2WNDq9oqGLUCdpxy4O31VoztrkNk8SiZ0iMec4pHsjSIF13b3OjnArh6egPK3FWZhTLOZJes4wSljjOEpv4KdZ1eGUJxfV/xR7aEfX9I+OhvN8YcYYIgYHBHA2ZtNrvrtg1FZe9V5uYp9yodlDpbuVcpVNLbda8iu+5VzmY7seZnGzqmaFAtTMDFPAmzt5qhUwvo2qhyH4OuKvdjpQ29TY3xX9vRvIaItEQBC/Y6lfo7TalPXlqtlM/jblfCMaitGoMX2zBvtwO3p4j45qgTRy7IeP1uZfsim4xZOxzo1lDpZMspkvHpficuFci4q7GyzYFzEn46L6FdXRFhWuDgnzImb7TjvmYioiqpTAtWel/pADTbgbf/HoZCq4xCq3K9VwsToBIFHL/kwNB5BeiUosHQjnrXnJsqERUaeEJ0rb744guMHTsWs2bNQrt27TBt2jTceeed+PXXX5GQkOB1n8jISPz666+uv4VSv1q89dZbmDFjBhYsWIB69erhX//6F+68804cPXq0TIdhoPh1d/P+++/DYlEm0n7llVeg0WiwY8cO9OvXD6+++mpAA3g1wzvrYbbJmPCVCfkWGW3qqDF7iOcvB2eyJeSY3L+89G6hQ7ZJxsyNZmQUD12ePSTCYzjy53ss+GCze7LwgXOUkY9v9A1D39Y6xISJmD0kAtPWF2HIvAI4JBkpCSp88Gg4GvsY3l+heHbSwWyXMWF1kTueA8M845njRE6R+0amd3OtEs9NZmQUKo+ZzB4U5hHPl+8yQBSAMV8UweaQ0SlFgwn3eA6rfvPBMExeZ8bIxSYIAnBbshqzB3kOz15x0Ia3vjNDloGWtdVY+Fi4x+MvN8N5AIClB2zo2URTZpGBYBveuTjeq0rEe3B42Xxe4rGE3i20yDZJmLnR4s7ng8M98/leq2c+n6vMY/PGg0b0ba1UyjM2mbHygHsF2Qc/VK6FBY+Ho129G3MOjrrN22DcJ+tdfz/8z7cBADuWL8SC8U+EKljXbPgdYUq5tjQP+WYJbeppMfuJap7pneX0LNdaGpBdKGHmt4XIKHCiSaIGs4dXQ1yE+1oY0iUMVoeMKavykVckIzVRjXlPxqJOnOe1unRPEVola1A/oXJHmNzo5Vww9E4VkV0kYeYOGRlFMprEA7P7iq7H4M4XyB6NhdaJAt7pLWLadglTt8tIjgbe7yOiUZxnOL/5VYYM4J7GvsO/9GcZrRKB+tUqvzOkdzMNsotkzPzepqRbdRGz/25wpdv5fAmC4E7D1rVVeOdBPaZttmLqZhuSq4l4/2E9GiW488Km4w78c5V7NOzY5cr/R3fR4OmuOmhUwI7TDqVDwgbUjBLQq7E6aPOG9W6mUfLmFmuJOBrdccyTIQjua7h1bRXe6Vscx02yEsdHDB5xHJ6uhdkGTPjaUnyNqDD7UaPHnI8/n3di5vdWFNmA+nEiJt2rx/0hnv8vGOdi068O/HOVu/4au0z5/+guWjxd/Ejz5/vs+GCru/4auMAMAHijj97VARkKoazTK0Moru8rlh20o0akgI4NbpxRo657lZWFSnrX1WD20Mjy0ztNV5zeZmQUKI8Ozx7qvlfRqgX8+LsdC7ebkW+RERsuom2yGp89GVVmwYZKjeNNfj9WUu+mKiWfb7MjwwQ0SRAw+2Gtu/7Olz1GKLauJeKdPhpM2+rA1O8dSI4R8H4/DRrFK/FVicDpLBnP/GxDjhmINgAtaopYPFCLhvFX0h1Y84sT7//ggM0J1IoSMORWNR67rXLzezDS+8h5Bw4VryDea2qex/dtGBeFWjEqfHvYhmyTjFWHbFh1yF22J0aL2PR8dBBjTKR477338MQTT+Cxxx4DAMyaNQvffPMN5s2bh/Hjx3vdRxAE1KhRw+tnsixj2rRpePXVV3H//fcDABYuXIjq1atj5cqVGDBgQFDiIciyfE2DpceOHYt///vfCAsLw9atW5Geng61OnCFq/xlh4Ad6y/FeWOt6kZBVpGVUv7CRr56INRBCIlZ71bRcs1kDnUIQiOvINQhCI2wKjofDyfTqVo0N9Zjt5XGZr/6NjcjfeV1mt5Qru228OZjuvp0VjelsKr5OK3Qf2eog3BDONT3xhzMUZkaf1YIq9Xq8Z5Op4NOV7YOsNlsMBqNWLp0KR544AHX+0OGDEFubi6++uqrMvvMnz8fw4cPR1JSEiRJQuvWrfHGG2+gWbNmAIBTp06hQYMGOHDgAFq2bOnar2vXrmjZsiWmT58emIiWcs0/Fc2cOROFhcovinfccQeys7ODEiAiIiIiIiIiIgqO0nPVVcXX5MmTERUV5fGaPHmy1/OVmZkJp9OJ6tWre7xfvXp1XLx40es+qampmDdvHr766it88sknkCQJ6enpOHfuHAC49rueYwbCNf+smZycjBkzZqBXr16QZRk7d+5ETEyM1227dOkSsAASEREREREREREFyssvv4yxY8d6vOdtFKC/OnTogA4d3E+Gpaeno0mTJvjf//6Hf//73wH7nut1zZ2Ab7/9NkaOHInJkydDEAQ8+OCDXrcTBAFOp9PrZ0RERERERERERKHk69Ffb+Li4qBSqXDp0iWP9y9duuRzzr/SNBoNWrVqhRMnTgCAa79Lly6hZs2aHscs+XhwoF3z48APPPAALl68iPz8fMiyjF9//RU5OTllXnxMmIiIiIiIiIiIbgZarRZt2rTBxo0bXe9JkoSNGzd6jPYrj9PpxM8//+zq8KtXrx5q1Kjhccz8/Hzs3r37mo/pj+ue5Tg8PBybN29GvXr1ArowCBERERERERER0Y1m7NixGDJkCNq2bYvbbrsN06ZNg8lkcq0WPHjwYCQlJbnmFXz99dfRvn17pKSkIDc3F2+//Tb++OMPDB8+HIDyFO2zzz6L//znP2jYsCHq1auHf/3rX0hMTPRYfCTQ/OrF69q1K06ePImPP/4YJ0+exPTp05GQkIC1a9eiTp06rtVOiIiIiIiIiIjoxiEIQqiD8JfzyCOPICMjAxMmTMDFixfRsmVLrFu3zrWwx5kzZyCK7odtc3Jy8MQTT+DixYuIiYlBmzZtsGPHDjRt2tS1zYsvvgiTyYQRI0YgNzcXnTp1wrp166DX64MWD0GWr38t+O+//x533303OnbsiK1bt+KXX35B/fr1MWXKFOzbtw9Lly697oDIXwZvuOMNzSmFOgRUmcSqWdiOfPVAqIMQErPeraLlmskc6hCERl5BqEMQGmGGUIcgNNh4rlo0VfTpF5s91CEIDX3gJob/S7n+28Kbg6ko1CEIjTBjqEMQEkL/naEOwg3h54eqaDlXQoul1lAHISSueU7AksaPH4///Oc/WL9+PbRarev9bt26YdeuXQELHBEREREREREREVWcX52AP//8s9fVgRMSEpCZmVnhQBEREREREREREVHg+NUJGB0djQsXLpR5/8CBA0hKSqpwoIiIiIiIiIiIiChw/OoEHDBgAF566SVcvHgRgiBAkiRs374dzz//PAYPHhzoMBIREREREREREVEF+DXL8RtvvIHRo0ejdu3acDqdaNq0KRwOBx599FG8+uqrgQ4jEREREREREREFANc3q7r86gTUarWYPXs2JkyYgJ9//hmFhYVo1aoVGjZsGOjwERERERERERERUQVdcyfg2LFjy/285KrA7733nv8hIiIiIiIiIiIiooC65k7AAwcOePy9f/9+OBwOpKamAgCOHz8OlUqFNm3aBDaEREREREREREREVCHX3Am4efNm1//fe+89REREYMGCBYiJiQEA5OTk4LHHHkPnzp0DH0oiIiIiIiIiIiLym19zAr777rv47rvvXB2AABATE4P//Oc/6NWrF8aNGxewABIRERERERERUWAIXBmkyhL92Sk/Px8ZGRll3s/IyEBBQUGFA0VERERERERERESB41cn4IMPPojHHnsMy5cvx7lz53Du3DksW7YMw4YNQ9++fQMdRiIiIiIiIiIiIqoAvx4HnjVrFp5//nn8/e9/h91uVw6kVmPYsGF4++23AxpAIiIiIiIiIiIiqhi/OgGNRiM+/PBDvP322zh58iQAoEGDBggLCwto4IiIiIiIiIiIiKji/OoEvCIsLAxpaWmBCgsREREREREREQUR1wWpuvyaE5CIiIiIiIiIiIj+OtgJSEREREREREREdJNjJyAREREREREREdFNjp2AREREREREREREN7kKLQxCRERERERERER/HYLIlUGqKo4EJCIiIiIiIiIiusmxE5CIiIiIiIiIiOgmx05AIiIiIiIiIiKimxw7AYmIiIiIiIiIiG5yXBiEiIiIiIiIiKiKELguSJXFkYBEREREREREREQ3uRtmJKB0OSfUQQgJMSos1EEIDVUV7X/OKwp1CEJi1rsdQh2EkBg5bmeogxASs95LD3UQQiMqItQhCA1T1SzXYLeHOgSh4ZRCHYLQsNpCHYLQ0GlDHYLQ4PVdtYQZQx2C0MgvDHUIiCgEqmhPDBERERERERERUdXBTkAiIiIiIiIiIqKb3A3zODAREREREREREQWXwJVBqiyOBCQiIiIiIiIiIrrJsROQiIiIiIiIiIjoJsdOQCIiIiIiIiIiopscOwGJiIiIiIiIiIhuclwYhIiIiIiIiIioiuC6IFUXRwISERERERERERHd5NgJSEREREREREREdJNjJyAREREREREREdFNjp2ARERERERERERENzkuDEJEREREREREVEUIXBmkyqrQSECbzYZff/0VDocjUOEhIiIiIiIiIiKiAPOrE7CoqAjDhg2D0WhEs2bNcObMGQDA008/jSlTpgQ0gERERERERERERFQxfnUCvvzyyzh06BC2bNkCvV7ver9Hjx744osvAhY4IiIiIiIiIiIiqji/5gRcuXIlvvjiC7Rv397jWfJmzZrh5MmTAQscERERERERERERVZxfnYAZGRlISEgo877JZOIEk0RERERERERENyj221Rdfj0O3LZtW3zzzTeuv69koDlz5qBDhw6BCRkREREREREREREFhF8jAd944w3cfffdOHr0KBwOB6ZPn46jR49ix44d+P777wMdRiIiIiIiIiIiIqoAv0YCdurUCQcPHoTD4UCLFi3w3XffISEhATt37kSbNm0CHUYiIiIiIiIiIiKqAL9GAgJAgwYNMHv27ECGhYiIiIiIiIiIiILAr5GAa9aswbffflvm/W+//RZr166tcKCIiIiIiIiIiIgocPzqBBw/fjycTmeZ92VZxvjx4yscKCIiIiIiIiIiCjxB4Kuq8qsT8LfffkPTpk3LvN+4cWOcOHGiwoEiIiIiIiIiIiKiwPGrEzAqKgqnTp0q8/6JEycQFhZW4UARERERERERERFR4PjVCXj//ffj2WefxcmTJ13vnThxAuPGjUOfPn0CFjgiIiIiIiIiIiKqOL86Ad966y2EhYWhcePGqFevHurVq4cmTZogNjYW77zzTqDDSERERERERERERBWg9menqKgo7NixA+vXr8ehQ4dgMBiQlpaGLl26BDp8REREREREREQUIEJVXhmjivOrExBQMk2vXr3Qq1evQIaHiIiIiIiIiIiIAszvTsCNGzdi48aNuHz5MiRJ8vhs3rx5FQ4YERERERERERERBYZfnYCTJk3C66+/jrZt26JmzZocSkpERERERERERHQD86sTcNasWZg/fz4GDRoU6PBct08PyZi3H8gsAlLjgFe6Amk1fHdKrvtNxsxdwJ/5QN1oYGxHoGuye/vMIhnvbQe2nwEKrEDbROCftwPJ0e5tMkwy3vkB2HEWKLIByTHAk7cCvVIqrzN08V4b5u60I7NQRuPqIl69S4e0JJXP7dcddWD6Fiv+zJVRt5qI57tr0bWhO/m/+8WBz/fbceSCE3lmYMUTBjSp4Xm8jEIJb2+wYccpJ0w2GfViRTzZSYs7m/g9oLT8OO6xYe52qxLHGiJevduAtFrlxPGIHdM3WfFnroS6sSKe76FD10Ya1+eyLGPmZiu+3G9HvkVG69oqTLxXj+RY5ZjnciT8d6sVu047kFkoIyFCwH1pGozsrINWraSt1S5j4tcWHLngxKkMCbc3UuODvxmDEv+SFh+QMHevhEwT0DgeeLW7Cmk1y8nnv0qYvl3Cn3lA3Rjg+S4iutb3XAfoZJaMd7ZK2HtWhlMCGsQCM+5XITFSOe6gzx3Ye87zuI/cImBST99pEGiyLGPmt4X4cncR8s0SWtfTYmLfKCTHl5/nFm83Ye4WEzILnGhcU4NXH4xEWh2txzYHfrdh2toC/HTGDlEEmiRqMGdENeg1AnafsGLIrGyvx/7ymVi0KHWsG0XDtp3Qa/hY1GnWCtHVE/HhP/rj0IZVoQ7WNVPSuwBf7iqR3v2ir57eP5gwd0uhkt6JGrz6YJSP9M5X0lsAmiRpMGdELPQaJb8fOWfDu1/n4+ezSn7olWbA+D6RCNP5tX7WdZFlGTPX5uPLXYXIN8tKvPvHIDleU+5+i7cVYO6mguJ4a/Fqv2ik1dW5Ps/Id+LtVbnY8asFJquMeglqPNkzEnfeUrbMsjlkPPzeJRw7b8eK56ujSa3g53FZljFzoxlf7rUoZXJdDSb2CUNyXPllzOJdFszdZkZmoYTGNdR49V4j0mor5yq3SMLMjWZsP2HDhVwJ1cJEdG+qxZgeBkTolbRcvt+Cfy4zeT329pdjEBse2DSv7LqsJJtDxsOzTTh2ScKKJ8PQpKa7vusxvbDM9p8PM6Jl7eDU6aUFsh1jd8qYvtmG7084cS5XQrhOQHo9FcZ216J6RPCv4fIs3mf3jOed2qvH83tbcTwFJZ4pJeK5xY7vTzhwLld2x7ObxhXPc7kS/rvNjl2/O5FpkpEQLuC+FmqM7KSBVlV57VRZljFzkwVf7rMq+bSOGhP7GL3m05IW77Zg7g/W4utbhVfvMSKtljtPfrHXiq9/suHoBQdMVmDPP6MQafBM427v5uF8rudTSmN7GjCiiz5wEbwS3hBc36M+LcKxi05kmWREGQR0qK/GuB46VI8sm9f/yJLw4P8KoRKAvS9HBjbyJVT2fcm5XAk9ZhZ5Pfa0fnrc1bRyyjFXPeaRz6+xHvvB4s7n94a58nlukYSZm8zYfsLurseaaDzqMQD4z9cm7D/jwG+XnGgQr8LKp6KCGleP8O93Yu4ep3JfkiDg1R4qpNX0XdauOyZh+g+O4vsSAc93VaFrA/f249c4sPKw5zXbqZ6AOf3d18aoZXYcuywjqwiI0gMd6ooY11WF6hEcjER0vfxqGdlsNqSnpwc6LNdt7XEZb24D/tEOWDoAaBwHjPgKyCqSvW5/4IKMF9YBfZsCy/4GdK8PPP018FuWsr0sy3j6a+BsHvD+vco2NSOBYSuAIrv7mC9/B/yeC3xwL7DyUaBnA2DsWuDoZe/fG2hrjtgxZb0No7tosfwJI1Krixj+qRlZJsnr9vvPOjFuuQUPtdRgxRNG9EhV4aklFhy/7HRtY7bLaFNbhee767weAwBe+sqK01kSPnxEj1VPGtGzsRrPLbPg6AWnz338juNhO6Z8a8Ho23VY/mQYUqurMPwTE7IKfcTxjAPjlprxUGsNVowMQ4/Gajz1uRnHL7nDNme7DYt22/DavXosGR4Gg1bA8EVFsBan7elMCZIMTLrXgK//EY6X79Tji312TN1odR3DKQN6DTConRYd6ldOZ9iaYxKmbJEwuoOI5YNUSE0QMHyp0vjzZv+fMsZ9LeGh5iJWDFahR4qAp1ZKOJ7h3v5Mroy/f+ZE/WrAwkdU+GqoCv/oIEJXKkr90wRsG6VyvV7oUrk3U3M2m7DoBxNe6xeFJc/EKWk2O9uVZt6sOWjGlFX5GN0zHMufjUNqohrDZ2cjq8CdFw78bsMTc7LRMVWHJWNi8eWYODza0QixuB3RKlmLbRMSPF792xlQq5oKzWuX3zETSlpjGM4d+wmfvT4m1EHxy5zNhVi0zYTXHorCkjHxMGhFDP8oq/z0PmDGlFV5GN0rAsufi0dqogbDP8oqm96zs9CxkQ5LxsThy2fj8WjHMFd6X8pz4vFZWagTp8YXY+Ix54lYnLhox8uf5wY5xoo5GwuwaGsBXutfDUueS1Dy+ayM8uO9vwhTVuZi9F2RWP58DaQmaTB8VoZHvF9anIXTlx34cHgcVr1YAz3TDHhufhaOnrOVOd7bq3KREFV5HfwAMGebBYt2WvDa/eFYMioKBg0wfH5++fH+yYopa0wY3c2A5aOjkFpDheHzC1x1w+UCCZcLJLx4VxhWPxONyf3Cse24Da8sd3f69W6hw7bxMR6vTg01uLWeOuAdgKGoy0p6e70VCeV0gn082Iht48Jdr2aJlVSvBbgdY7EDRy9K+EdnDZYNN2Jmfz1OZ0n4xxeWSomPL2uOOJR4dtZg+XCDEs/PLL7r77NOjFthxUMt1VjxhAE9UtV4aokVxy8r50WJpxP/6KzFsuEGzHxIp8RzibudcjqruC3TW4uvnzTg5V5afLHfjqmby173wTRnmxWLdlnxWh8jljwZoeTTBYXlX98/2zBlrRmj79Bj+ahI5fpeUOhxvVjsMjo31ODJLoZyv/+ZbnpsezHK9RrY3nf71l+hur7b1VNhan8D1j4djukPG3AmW8KYJeYy32d3yhi3rAht6wS3QywU9yU1IwVse87o8Xq6qxZGLdA5pfLqsjnbLEo+vz8MS0ZGFufzgqvkcyumrC3C6DsMWP6PKKTWUJetx/IlvHiXEaufjsLkvmHY9psdr6wo++NVv9Y69G5RuT9Mr/nFiSmbnRjdUYXlQzRIjRcwfImjnPsSCeNWO/BQCxVWDNWgR0MBT61w4HiGZ/7oXE/Atn9oXK937/PMt+3qiJh6vxprh2sw/QE1zuTKGPOVI2jxrAoEka+qyq+oDx8+HJ9++mmgw3Ld5h8A+jcH+jYVkBIrYGI3QK8Glh/1vv2ig0CnusCwNgIaVBPwTAcBTeOBxYeUz//IBQ5dBCbcAbSoLqBejICJdwBWB7DmV/dxDlwEHk1TRhzWjhIw8jYBETrg6OVgx1gxf5cd/Vtp0K+lBinxIibdo4NeI2DZQe8F4aI9dnRKUWFYuhYN4kWMuUOHpjVFLN5rd21zf5oGo7to0aGe74rz4FknBt6qQVqSCrVjRIzqrEWEHjhy0XslX6E47rSif2sN+rXSIiVBhUn36pU4HrB73X7Rbhs6pagxrKMODeJVGNNNj6Y1VVi8R2n0yrKMhbtsGNlFh+6NNUitocKbDxpwuUDGhmPKeevcUI3JDxjQKUWN2tVEdGuswePpWqz/xf2dRq2A1+414OE2WsQF+GbRl/n7JPRvIaBfCxEpcQIm9RSh1wDLDnuvbBftl9CpnoBht4loECtgTCcVmlYHFh90p9O0bRK61hfwQlcVmlYXUCdaQLcUEbFhnr+mGdRAfJjgeoXrKncUwcJtJozsEY7uzfVITdTgzQHRuJzvxIbDvm/s5n9vQv92RvS7zYiUGhpM6hel5J297gbylFX5GNQpDCO6haNhDQ3qJ6hxd0uDa8SnVi0gPlLlekWHidh42Iq+txpu6OkPjmz9Fl9New0H1/91Rv9dIcsyFm41YWSPCHRvblDS+2/XkN5bjg+GlAABAABJREFUC9G/vZf03uMeITDlqzwlvbtHeE3vLUctUKsETOgbhfoJarSoo8VrD0Xju58s+CMzuA1MJd4FGNkrEt1bGJCaqMWbj8bicp4TG34ue1PniveWAvTvEI5+7cKVePePgV4rYtlu903CwdM2DOwcjrS6OtSOU2NUryhEGEQcOevZGbD1qBnbj1nw4v3RwYpmGbIsY+F2M0bebkD3plqk1lDjzf7huFwgYcMvvjsr5m+3oH9bHfq10SMlQY1J94cp5eGPSidIo+pqzPx7BLo10aJOrArtG2jwXE8jNh+zweFUyky9RkB8hOh6qURg9yk7HmoT+FFCoajLrtj6mx3bTzrwYi/fnR/RBs9zoamkkWKBbsdE6AXMG2jA3c00qB8nomUtFf51tw5HLkg4nxf4Nsq1mr/bjv6t1O549tYWx9NH+u+1o1MDFYZ10KJBnIgxt2uVeO4rEc9HDbi7qRr1Y4vjeZfWI56dG6gxuY8OnRqoUTtGRLdGajzeXoP1xwL/g60vsixj4U4LRnbVo3uT4uu7X1jx9e097gAwf0fx9d1ap1wv9xmV63u/u0wYkq7HiC563FK7/I6eMJ1n3jZqA5+3Q3V9D+2gQ8vaaiRFi2hdR40RnbQ4dM4Ju9OzXTh9kxX141S4q1lwOwFDcV+iEgXEh4serw3HHLi7qRphQUhrb2RZxsIdFoy8vUQ+fyjsOuqx4nzex+i9Hmtcuh6zu+oxAHj13jA82l6PWjGV25Mxf5+E/mki+rVQKfcld6qU8P/svaxdtK/4vqSdSrkv6axG0+oCFu/33F6rAuLDBdcrSu+ZjkNvVaFlooikKAGtk0SMaKfCofNymXxPRFfnV6lhsVjw3nvvoWvXrnj66acxduxYj1dlsDllHL0MtK/tfk8UBHSoDRy84H2fgxeADrU93+tYV+n4U46p/FtyNJQoCNCqgP0ljtmqBrD2NyDXIkOSZaw5LsPmAG6tVfF4XY3NKePIBQnpJSpFURDQoZ4KB895b+AdPOf02B4AOtZX4eC562sYt6ytwpqjDuSalXh/c9gOmwO4rW5gf3GzOWQcOS8hvb670SKKyuMOPuN41on0UiPzOqa4z8m5HBkZhbLHMSP0AtJq+T5vAFBgUR61CBWbU8aRS0B6XXcYREFAhzoCDp73XukdPC97bA8AHZPd20uyjC2nZCTHCBi21In0Dxx4+BMHNvxWNj+s/kVG+w8cuO9jB97d6oS5nF82A+1cthMZBRLSG7pvYCMMItLqaHHwD++NK5tDxpE/7Uhv5N5HFAV0aKhz7ZNV4MShM3ZUCxcxYGYmOr52CQM/zMKPp3032DYdsSC3SELfW4P/6HdV5UrvRteZ3ufsHnlEFAV0aKTDwT+UmwmP9J6RgY4TL2LgB5n48ZTV4zgalbLvFVceE/7xVHBHz5zLciIjX0J6I3cHVIRBRFpdHQ7+bvW6jxJvW9l83shzn5b1tFhzoAi5JickScY3+4tgc8i4LcW9X2aBE//6IgdvDnQ/Gl0ZzuVISpncwD2yNkIvIq2WGgfPeL9xVOoGB9JT3KMeRFFAhxQtDp7x3bFQYFEem1T76OBaecAKvUbAnc0DO5oilHVZZqGEf62y4M0HDeWm6z8+K0L6WwX4+1wTNh3zfQ4DqbLaMQUWGQKASH1o6nCf8UxW4eCf3sN98JzkRzxx1XgWWFCpbRn39V06n6px8Gx517ez7PXSQONzn/LM3mZBuzdy8eAH+Zj7g8Wj8yQQbpS2am6RjNU/29GqtsqjE3/XKQfWHbFjQu/A/7hRUijvS0o6fMGJXy5J6Ney8p7WKLceu1o+L7HPteTzAotUbj1WWWxOGUcuykhPdnchiIKADnVFHDzvo1w7L3lsDwAd65W9j9lzVkb6+zbcNduG175zIMfs+5rNNctYfVRCqySh0n68IrqZ+PXT0E8//YSWLVsCAA4fPuzx2bWMkrFarbBaPW9u1HYJOs2190nmmpVHM+NK3ZPHGoFTOd73ySxSPi8pzghkFg+cqBcD1IwApu4AXusmw6ABFh4ALhYCGSVGYL/XGxi3Fkj/CFCLyujDGfcAdaODXwjlFMlwykBsuOd3xYUJOJ3pvfDNLJTLjPCKCxeRabq+RtW0fno8t8yC9u+YlHhrgJn99ahbLbC/QJUfR+8NisxCucxjXHFhIjILlQoko3iIvbdjZvp4bOOPLAmf7LHhxV7BbUCVJ6c4n5dJvzDgdLb3yjHT5C2fC8gsHqafVQQU2YHZuyWM6STi+S4itp2W8fRXEhY8IuC22sp33dtERGIkkBAu4HiGMn/g7zkSZt5fOY9ZZBQUp1mpR9niwkVkFnhPsxyTBKeEsnkhQsTpy0p+P5ut5KH3vyvAi/dGokmSBl/tM2PorCysfj7e6/xzy/aY0SlVhxrRlfu4ZFWSke8jvSNEZOZ7v+5d6R3hmS5x4SJOX1Y67zzS+74oNEnU4Ksfi5T0fiEByfFqtG+ow5ur8jF3cyEGdQ6D2Sbj3W/ylXAVBHf0zJXjl4mDP/GOUOH0JXe5Pm1IHJ5bkIX2r5xXymytgJmPx6Fu8VyDsizj5cXZGNAxDC3qaHEuq/Ieq3Fd36Wv1XDRZ5mcUyQXX9+l6zMBpzO8l4c5Jgn/3WLGw7f6LseX7bPi3jRtwDtBQ1WXybKMl1eaMaCtFi2SVDiXU/Z8hmmBl3rp0LqOCqIg4Lujdoz+3IwPBgDdGgf3Jroy2jFWh4x3NtpwT3N1pY5gL8kVzzLhFnA66zriGSYg08djlVaHjHc22XBPM5XPeP6RLeGTfXa82L3yHhnMKM6vZfOy7zaX+/ouXSb4vl58GdReh6aJKkQbBBw448R76824XCDh5bsD90NeqNuq76y3YPEeG8x24JZaKsz6u/vx6JwiCS+vNOOtvgaEB7kTPJT3JSUtO2BHgzgBra8yQjSQ3OnlrZ3qo07yWY+JOJ3p/YeYHJOE/2624OFbA/9I+/XKKSq+Lyl9nxEGnPY+lbb3+5JS5VrnegJ6NVQjKRo4mwtM3erAiC9lfD5QDVWJH2jf2eLA4gOSku8TBczqVzlzPxLdbPy6cjZv3lyhL508eTImTZrk8d6/7o7FxHviKnTcitKoBMy4R8arG4AOHwEqQRk52LkuULIon7ETyLcCcx8EYvTAxlPKnICLHpLRKO7m/TVi+hYbCiwyPh6oR4xBwIZfnXhumQWfDDEgtfrN1TlyKV/CE5+YcFdTDR5uc2MuAuEvqTgzd0sRMLSt0nBpkiDgwHkZnx+ScFtxA+qRW9yNmtR4AfHhwNAlEs7kyqgThA7v1fvNmLg0z/X3rGExAf8OwB3/R4ofIQWApkka7DxhxbK9RRjX23Pi7Iu5TvzwqxVTB0UHJTxV1eofizzTe3i1oHyPVNzGfKRDmDu9a0Vh529WLNtThHH3RKJhDQ0m/y0ab67Kx3tr8iEKwKDOYYiLECEGOKuv3mfCxCXuX6pmjQhevTd9bR4KzBI+/kc8YsJEbPjZjOfmZ+KTZxKQmqjFoq2FMFkljOgRvMnir1h90IqJX7kXopg1OPjfWWiR8OTCAjSIV+Gp7t7nDztwxo6TGU682T886OGpLIt222CyAiM6+667YsJEPJbuvqFskaTC5QIZc3fYgt4JGGx2p4xnlypTCLzWO/Q3zcFid8p4dpnyg7qveF7Kl/DEZxbc1USNh1sHL11XH7Ji4ir3FAyzBob2enqso7vTP7WGGhoVMHFVEcb1dE8D8Vc3LF2Lfq00OJ8n44MtVoxfYcGsvytTlkxYZcG9LTS4NblqdJBY7DK+PuzAqHLKvEBYfdCKiavco0JmDYoI6vcBQKFFxpOLCtAgQYWnupU/D+Zf2T1N3PeRqfFAarwGPT+yY89ZGR1KPN007DYV+qWpcD5fxgfbnRj/jQOz+qlv6Kl6iG5EIakdXn755TKPDavntb2uY0QblE66zFILQ2UVlR0deEWcUfm8pMwi5deLK5olCFjxd6DAKsPuBKoZBTzyhYzmCcrnZ3JlfPoT8NWjQMNYpcBpHA/8eF55/7Vu1xWN6xZjFKASgKxCz1+YMk0y4sK9F4Bx4UKZyVozCyXEhV17gXkmW8LivXasftKAhglKQd24hgo/nnXi0312TLoncJ2A5cfR+6jDuHChzETMmSbJdU7ii/fLKpSREFFyG7nMKsiX8iUMnl+EVrXVeP2+0I0CBICY4nyupF+JVaxN8Jl+cWHe8rnsyucxBmUEa0qs5/4NqgE//ul76P2VVbf/yAlOJ+AdTXVIG+vuELE5ikcuFkhIiHSnUWahhCaJ3ouumDBlfq8yeaFAQlzxqnlXJslPqe55jAYJalzIKfvr/fK9RYg2iujWLLR54WZzRzM90uq6G+w+07tAQpMk7zevrvQuNVovs1BCXPEouYTIa0vv+1obcV9rIzILnDBoBQhQ5pesHRvYavKO5oZS8Vb+zSpweizM4Ve8C5yufH4m04HF2wqx+qUaaFhTOU7jJC1+PGXFpz8U/j979x0eRbWwAfyd2Z7egRQIEDqEUBRBihQREUVBFBUBNSiKimJD5Qp47ydw7WJBEaRcFFGKooJ0RQgIAiIBRJAWCKQnu9m+M98fE7JZsgkQNtmYvL/nmQd2MzN7zp4zc8qeOQfT74rAzr9s2HfCjuRnPZcBv/Ot8xjSJQCz7ou86vhe0LeNFskJYaWvS9PbJJWmEVByfTeq4PoOEEqu74vLs/Lln8kmI3WhEYE6Ae/fF1zh40Jf77ahTSMV2sf5vjrkr7Js53EX9mW4kPxvo8d57vykGEOSNZh1h/eGZHK8Ctv/rv7RoNVZj3G4ZDy93IqzhTIW3G/w2yhAoEw8y4X7CuNZLCMq0DO/OFwynl5hU+I5Su81nueNEkb/z4pO8Sq8ekv1do70ba31WMG39L5mkjwWpvFW57rAfX1flP9NFV8vlys5Xg2npDy+2SzaN/VVf9dVwwNFhAcCTaOA5lEibnjbhH0ZLnRKUGPHcSc2/QnM314y1yCUH0DbTS/Cq7fqMbyz7/KDv9olZf14yAmrA7g9uXp/wFDKsbL5vEw5FnxxOXapfO4l/hflm9JyTCvg/XuDasVjr+EBJe2Si9sZxZ7t6bK8tkuK5UrTOyFMQLhBaXN0b1L28wWEBwBNIwQ0jxRww0cO7Dsro1Oc/7+bfyJ2ntZfVS5Vd+/ejeeffx4jR47EsGHDPLZL0el0CAkJ8diu5FFgANCqBLSNAXacdr8nyTJ2nAZSGnk/JqWR5/4AkHYK6Niw/L7BOgERAQJOFMhIzwL6NVPet5ZUbC4eHaISALkGpkvTqgS0ayQi7YS78SfJMnYcdyEl3nuBkxKvQtpxz8bi9uMupMRf/nd+YS448aKbhSi4R1b5ilYtoF2siLTj7saIJMnY8bez4jgmeInjMfd3Eh+uTDJb9pwmq4z9GZ7f24UOwHaxIl67Xe8xR5g/aFUC2jUA0k65v2RJlrHjlIyUWO9hS4kVkHbSM1G2n3Tvr1UJaN8QOJ7vuc+JfCA2pOL4Hs5W/o2poFJ3tYL0IppEqUu3pAZqRAeLSPvLPXWAySph/yk7Upp4r8Bq1QLaxWk8jpEkGTuO2kqPiYtQISZExPFsz/xyItuJ2HDP/CXLMlbssmBoV0OtqHzVJT5L73gN0v5yz9snSTJ2/GVDShOlMVCa3lmenRsnsp2IjSh/P4kKViFQJ2LNPit0GsFj3j1fCNKLaBKtKd2SGqoRHSIi7S/34icmq4T9J21ISfT+2Uq8teXz+RH3MRa70tC8+BYmCkLpPfvl4WFY9XwDrHxO2T5+OBoA8NaYSDx9S6ivogwACNIJaBKpKt2SYlTKPflv9+NPJquE/RlOpFSwkqVSNqiRdsx9jCTJ2HHMgZTG7safySrhoc+KoFEBH44Khq6Cx3yLbTLW/GHD8C7VM1rMX2XZyzfrsWp8IFaWbB/fp/wy+tYIA57uV3FcD59zIbqa7u9lVVc95kIH4Mk8GZ+NMiA8oBaU341Ej3BLsowdJ1xIifNe/0qJ9/xegAriucKGk3kSPrtP7zWe54skjF5sRbuGIl67VVuu7uZr5a9vseT6vjifOpGSUNn1rfI4RrleHBUec7kOn3NCFMo/gnk1/FlXvdiFe/qFztelqe7rf+X4QDzZV4dALZQVidv4tqPMX+2Ssr7e50DflipEVLET8XJVWI4dK1uOXW4+v6gcuyifm6wyHlpw6XKspmlVAto1FJB20t2ZLckydpyUkBJbwX0tVvTYHwC2n6i4HQMA54wyCixATCVpWprva27NI6I6o0ql6tKlSzF69GjcdNNNWLduHQYOHIgjR47g/PnzuOOOO3wdxgqN7QS8uB5o30BGhwbAon2AxQnc0Vb5++R1MmICgUnXKzeQ+1OAMcuBz/bI6JMI/HAEOJAFTO/vPufav2REGJS5AY/kADN+Bvo3A64vGYrcNBxoHApM2wQ811NGWMnjwNtPAR/eVkPxvk6Dyd/Y0L6RiORYFRb+aofFIWNYRyU5X1hlRUywgGf6K5X9+6/VYPQiC+an2XFDCzW+T3cg/ayEV29xNwYKLDIyCyVklcxhcWG+mqggZfWtZlEimkQImPqDFc8P0CHMIGDDn05s/9uFOSN9P0JqbHcdJq+0oH2sCslxKizcURLHTkrl5YUVFsSECHhmgPLZ93fTYvQCM+ZvtylxPOBA+llX6Ug+QRAw+jot5vxsQ2KEiLhwEe9tsiEmWMCA1sr3dqEDMDZUwAsD9cgrlnHhQfDoMr/wHc1yweECCi0yiu0yDmUqpU9Fv/pd9XfRVcTkNRLaN5CQ3EjAwt+UuTCGtVfy5As/uBATBDzTW/n8+zuLGP2lC/N3SbihmYDvD0tIPwe8eqM7Dg9dI2LSagld4yV0SxCw9biMzcdkLLpbOcepAhnfHZLRu6mAMANwJFvGjM0SusYrjwbXBEEQMLpXIOZsNCExWo24CBXeW2tETIgKA9q789zYObkY0F6PUT2VnyDH9gnE5KUFaB+vQXJjDRZuNcNilzHsGkPpeR+6IQiz1xnRqpEabeI0WLXbgr+znHh3tOcjyDuO2pGR58KIbv+MBUF0AYGIbtK89HVUfCLi2ySjuCAf+ZmnKznS/wRBwOjegZizwYjEKDXiIlV4b42X9P4oBwM6GNzp3TsIk5fmo31CSXr/XKykd8mjv4Ig4KG+QZj9oxGtYjVKeu8yK+k9xp2u//ulGJ0SNQjQidj+pw2vf1eESbcEI8RQvavuKfEOxpx1RUiM1ij5/IcixISqMKCDe8TW2A+yMCDZgFG9lOEhY28IxuTPc9E+QYvkxlos/MkIi13CsG7K99KsgQZNotSYuiwfzw8NRVigChv+MGP7ESvmjFNG3MaGe1YBArRKC7JxpBoNw6r3QQFBEDD6egPmbLYgMVKl3JM3mBETLGJAG3en79h5hRjQVotR3ZXvYuz1ekxebkL7OBWS49VYuN2qpHdJR57JKuGhBUZY7DJeHxEMk02GyabcxyMCBY+5hdb8YYNLAm5Lqb5HRv1RlsWGeebZAK1SnjcOF9EwVPnbyn12aFQC2jZUXq875MTyvQ78+7aaGfHs63qMwyVj4tdWHDwnYc7derhkuXReulCDAK2ffsQZ202Dyd+WxDNOhYU7HSXxLEn/b5S0e6afkufvv0aD0YutmL/DgRuSVPg+3anEc3CZeC634WCmhDkjdV7jeaEDMDZUwAsDtMgzl6nLXOWIusslCAJGd9djzhZrST5V4b2NlpLr290JNfYzIwa00WDUdUq+G9tDj8kripXrO06NhWlWWOzAsDIj17KNEnJMEk6V1FOPnHchUCehUaiIsAARe085sT/DiW5N1QjUCdh32okZayy4taMWoT6+n/vj+v49w4k/zkjo0liFEIOA03kS3t1sQ+NwAZ1KpnNpftFoxwNnXRAFoGU1Td3jj3bJBSfzJOw+KeGTe2r+aQ1BEDC6R0k+v1COlebzMuXY/CKlHLuQz6/XY/LyYrSPVZcpx1CmHFM6AC0O4PV7Ayssx07mumC2y8gxybA6ZRzKVMrw5tGqan3sfWxXEZN/cKF9QxeSG4lYuNultEs6KOnywvdOpV3SR0n/+7uKGP2FE/N/deGG5iK+P+RC+jkZr96k5Mdiu/Jo78BWIqICBZwukPH6FhcahwM9myrx+P2shD/OyegSJyBEr+zz7i8uNA4DOlXSmUhE3lWplv/aa6/h7bffxoQJExAcHIx3330XTZs2xSOPPIJGjSoYhlcNbm4pIM8iY/YOZRhy62jg46HKIggAkGn0HAXRqZGA/94k47004J3tQJMwYPYQ92O9gLIAyH+3Ko8JRwcCQ1sD4691n0OjEjBnqIy3twETVisLLDQOA2bcCPRJrJmb0OB2GuSZZcz+yY5sk4w2DUTMvddQOoz8bJEEQXAXkJ0TVHjjDj3e2WzD25vtSIwQ8f5derSMcVcGNh1x4qVv3aNKJq1Q/j+htwZP9NFBoxLw8UgD3txkw6NfWmG2y2gcLmLmUB36tPB9Y3Fwew3yimXM3mxT4thQxNxRAe44Fl4Ux8ZqvDHcgHc22fD2RqXy9P5Ig0eFJ/V6LSx2Ga+stqLIKqNLYxXmjgoo/XVt2zEnTuZJOJkH9HnL5BGew9Pc81c9vMSMs4XuUXR3fFxcbh9fGtxaRJ4ZmL1NQrYZaBMNzL1TVTqM/myR7DGcu3OcgDduEfHOLxLe/gVIDAPev11EyzKddze2EDHtRuCTnRL+b5PSuf3eUBFd4pV9NCKw/aSEhb8BFofSKT6wpYBHr6uZBsQFqX2VRRpe+boQRRYJXZpqMXdchMcvoqdyXcgvM7nw4BQD8kwSZv9oQrbRhTaxGsxNjSh9PBQAxvQOhM0pY+a3RSg0y2gVq8b8RyLROMozL3/9qxmdEjVoFvPPmFenSfsueOZ/60tf3/XS6wCA7SsWYeHkcf4K1mVL7RtUkt4F7vR+ONJLert/9h3cyYC8YgmzfzQiu8iFNnEazB0XeVF6B8HmkDHzm0IUWmS0alQ+vf84ZcfsH4tgtsloFqPG9DtDMbRrzXT+pvYPVuL9ZZ4S72Y6zH0k2jPeOU7kl3mMbHDnAOQVuzB7TWFJvLWY+0h0abw1KgEfPxKFN1cX4tG5Oco9O0qNmfdGoE/b2jGvUGovvRLvVSblntxEg7ljQzzjnSch3+y+3w5O1inpvdGCbKPy6PDcscGlZUP6WRd+L1lhceBbBR6ft+HZMMSXGe379W823NhOW60dvf4oyy7XRz/ZcLZQgkoEmkWJeOtOAwa1q5n5AH1djzlvlLHpiHJfuH2uxeOzFt6vRzc/zY02uJ26JJ4OZBfblXjeoy99TLJc+ieo8MbtOryzxV4STwHv36VDyxhlH894Wj0+a+EoPbolqrDtuAsn82WczJfR5z3P7+LwlAqe16sGqb10sDhkvPKtuSSfqjF3dFDl13cHbcn1bUV2ySOVc0cHeTwmuXSXDR9sdsd91DylvvbaHQEY1lkHrRr44Q873t9shd0pIz5cxJjuOo95An3FH9e3XiNg/SEHZm+xwWKXER0soFeSGo+O8N98h/5ol1ywfJ8DDUMEXN/cP3OTl5Zj3xS78/mY4PL5vGw9tYNOyTcbLe58PqZsOebE7yUrKw98u9Dj8zY8E1pajk1ZWYxdJ9yjRu/4oKjcPtVhcBsV8izA7F9cyC52oU2MgLkj1JW0S0S8MUSNd7Y68fZWFxLDBbx/hxoto5X4qgTgz2wZq9KdMFqB6CDg+kQRE3upS/O0XgOsPyJh9i8yLA5ln15NRTx6m7rOzPNJVJMEWb7yh1gDAwORnp6OxMREREZGYsuWLejQoQMOHTqEfv36ITMz84oD4vqg9RUfUxeIoTVXIatVVDXbmVRrmMyX3qcuahjt7xD4xfhn0vwdBL+Y81YPfwfBP1R1a4Gky1ZcT+9rDu8rOdZ5Lu8rftZ5NTHnS22kq1uLo102Vz19xrC+Xt/1NZ8XmS69Tx0kPPSHv4NQK5x4qPoXaavtEucV+TsIflGlnpjw8HAYjcqE03FxcThw4AAAoKCgAGZzPW0MEBERERERERHVcoLArb6q0vMRvXv3xvr169GhQweMGDECEydOxKZNm7B+/Xr079//0icgIiIiIiIiIiKiGlOlTsD3338fVqsyH8fLL78MjUaD7du3Y/jw4ZgyZYpPA0hERERERERERERXp0qdgBEREaX/F0URkydP9lmAiIiIiIiIiIiIyLcuuxOwqOjyJ00MCeEkk0RERERERERERLXFZXcChoWFeSz37Y0sK0uCu+rrilpERERERERERLXYpfp2qO667E7AzZs3V2c4iIiIiIiIiIiIqJpcdidgnz59qjMcREREREREREREVE2qtDDIBWazGadOnYLdbvd4Pzk5+aoCRURERERERERERL5TpU7A7OxsPPDAA1izZo3Xv3NOQCIiIiIiIiIiotpDrMpBTz31FAoKCrBz504YDAasXbsWCxcuRIsWLfDtt9/6OoxEREREREREROQDgiDU+62+qtJIwE2bNuGbb75B165dIYoimjRpghtvvBEhISGYMWMGbrnlFl+Hk4iIiIiIiIiIiKqoSiMBi4uLERMTAwAIDw9HdnY2AKBDhw7Ys2eP70JHREREREREREREV61KnYCtWrXCn3/+CQDo2LEjPv74Y5w5cwZz5sxBo0aNfBpAIiIiIiIiIiIiujpVehx44sSJyMzMBABMnToVgwYNwv/+9z9otVosXLjQpwEkIiIiIiIiIiKiq1OlTsBRo0aV/r9z5844efIkDh8+jMaNGyMqKspngSMiIiIiIiIiIt+px+ti1HtVehwYAObNm4f27dtDr9cjPDwco0ePxqpVq3wYNCIiIiIiIiIiIvKFKo0EfOWVV/DWW2/hiSeeQPfu3QEAaWlpePrpp3Hq1Cm8+uqrPg0kERERERERERERVV2VOgE/+ugjzJ07F/fcc0/pe7fddhuSk5PxxBNPsBOQiIiIiIiIiIioFqnS48AOhwNdu3Yt936XLl3gdDqvOlBERERERERERETkO1XqBLz//vvx0UcflXv/k08+wX333XfVgSIiIiIiIiIiIt8TBKHeb/XVZT8OPGnSpNL/C4KATz/9FOvWrcN1110HANi5cydOnTqF0aNH+z6UREREREREREREVGWX3Qm4d+9ej9ddunQBABw7dgwAEBUVhaioKKSnp/sweERERERERERERHS1LrsTcPPmzdUZDiIiIiIiIiIiIqomVZoTkIiIiIiIiIiIiP452AlIRERERERERERUx13248BERERERERERPTPJnA4WL3FpCciIiIiIiIiIqrj2AlIRERERERERERUx7ETkIiIiIiIiIiIqI5jJyAREREREREREVEdx4VBiIiIiIiIiIjqCUEQ/B0E8hOOBCQiIiIiIiIiIqrjas1IQLFrir+D4B9mo79D4B/Zuf4OgX8kxPo7BP5RUOTvEPjFnLd6+DsIfjF+0nZ/B8Ev5rzU2t9B8AvLmQJ/B8EvDK3i/B0EvyjY97e/g+AXYSkJ/g6Cf4j1c7yA82z9rKeqwwL8HQT/iIn0dwj8I7qexpuonqufJTsREREREREREVE9wk5AIiIiIiIiIiKiOq7WPA5MRERERERERETVi+uC1F8cCUhERERERERERFTHsROQiIiIiIiIiIiojmMnIBERERERERERUR3HTkAiIiIiIiIiIqI6jguDEBERERERERHVEwJXBqm3OBKQiIiIiIiIiIiojmMnIBERERERERERUR3HTkAiIiIiIiIiIqI6jp2AREREREREREREdRwXBiEiIiIiIiIiqie4MEj9xZGAREREREREREREdVyVRgJ+++23Xt8XBAF6vR5JSUlo2rTpVQWMiIiIiIiIiIiIfKNKnYC33347BEGALMse7194TxAE9OzZE6tWrUJ4eLhPAkpERERERERERERVU6XHgdevX49rrrkG69evR2FhIQoLC7F+/Xp069YN3333HX7++Wfk5ubi2Wef9XV4iYiIiIiIiIiI6ApVaSTgxIkT8cknn6BHjx6l7/Xv3x96vR4PP/ww0tPT8c477+DBBx/0WUCJiIiIiIiIiOjqcF2Q+qtKIwGPHTuGkJCQcu+HhITg77//BgC0aNECOTk5Vxc6IiIiIiIiIiIiumpV6gTs0qULnnvuOWRnZ5e+l52djeeffx7XXHMNAOCvv/5CQkKCb0JJREREREREREREVValx4HnzZuHoUOHIj4+vrSj7/Tp02jWrBm++eYbAIDJZMKUKVN8F1IiIiIiIiIiIiKqkip1ArZq1QoHDx7EunXrcOTIkdL3brzxRoiiMrjw9ttv91kgiYiIiIiIiIiIqOqq1AkIAKIoYtCgQRg0aJAvw0NERERERERERNVE4Mog9VaVOwE3btyIjRs3IisrC5Ikefxt/vz5Vx0wIiIiIiIiIiIi8o0qdQJOnz4dr776Krp27YpGjRqxF5mIiIiIiIiIiKgWq1In4Jw5c7BgwQLcf//9vg4PERERERERERER+ZhYlYPsdjt69Ojh67AQERERERERERFRNahSJ2Bqaio+//xzX4eFiIiIiIiIiIiqkSBwq6+q9Diw1WrFJ598gg0bNiA5ORkajcbj72+99ZZPAkdERERERERERERXr0qdgPv370dKSgoA4MCBAx5/4yIhREREREREREREtUuVOgE3b97s63AQERERERERERFRNanSnIBERERERERERET0z3HZIwGHDRuGBQsWICQkBMOGDat03xUrVlx1wKpKlmXMXnEOX23JQZHZhc4tAjF1bAISG+orPGbXYRPm/XAe6SfMyC5w4v2JTTGgS5jHPjmFDrzx5VlsO1AEo9mFrq2CMOX++ErPW9NkWcbs1Xn46pciFFkkdG6ux9R7opHYQFvhMbv+smDeunykn7Ihu9CF98c3xICUII99Zq/OxQ+7TTiX74RGLaBdYx2eGhqJjk1rPu6yLGP2Jiu+2m1DkVVG58ZqTL0tAImRqkqPW7LTinm/2JBjktC6oQpTbglAcrw7+3+5y4bv9ttxMNOJYhvw60uhCDGU7yPf8qcDH26x4M9zLujUAq5JVOOD+4LK7edrsixj9ppCfJVmQpFFRuemWkwdEYHEGE2lxy3ZasS8TUXIKXKhdZwWU4aHI7mJrvTvp3Ic+O+qAvz2tw12p4xebQyYMjwcUSHu73POukJsSbfg8BkHNGpg18yEaovnxWRZxuzNVnz1m92d3kMMl5HeNszbbkWOSUbrBipMGWzwSG+bQ8asHy34/oADDpeM65trMHWIAVFBnmm+Yq8NC9JsOJErIUgnYFBbDV4ZElAtcS1LlmXM/tGIr3aYlWu5qRZTh4chMbryW/aSX4oxb4sJOUYXWsdqMOWOUCQ39rz+956w4501Rdh/ygFRANrEafDpw5HQa5SpHNIz7HjzuyL8cdoBUQQGJhsw+bYQBOpq729GLbr2xMDUSWjcrhPCGsTiw8dG4PcN3/o7WJdtyW9OzNvpRI4JaB0jYMpADZJjK/6+1x5y4d2fnThTKKNJhIBnb1CjT5L3a2LqWge+3OvCi/3VGHOtO//M2ebElmMuHD4vQ6MCdk3yf1mm7jIa6usehhAUDen8ITjWTYV09nev+wpRLaDp8wzEhu0hhiXAvm46nLvme+6kDYSmzzNQtboJQkAUpPPpcKybBilzfw3EpmKyLGP2Rgu+2mVV7mtNNJh6WyASoy5xX9thxbytlpJyTI0pQwKQnKCUAQVmCbM3WrDtqB2ZBRIiAkX0b6vFxAEGBOvdeemPDCfe/LEY6WddEAB0iFfjuUEBaN2oSg+GXBVd9weg6/0YxOBouDIPwvzNy3Bl7PW6r/ba+6DtPAKqBq0BAK4z+2FZO8Nj//BZ57wea/7+Vdh+/tD3EfBiya92zNtmU8qehiKm3GxAcnzF6bo23YF3N9lwpkBCk0gRzw7QoU9Ld7mulIE2fLXHoeSVBBWmDtF7lIH93jbibKHscd5J/XV4uJdS1tscMqZ+Z0V6pgt/Z0u4oaUaH9xTQ+WYn/L5BflmCbfPLsT5Igm/Tgn3Wq+rbmLyfRC7pgIB0ZBzDkPa/Crk8xXcgyKSoOr+FIQG7SCExMP10/9B2rvA83zXPAKh+UAIEc0Apw1y5h64fnkdyD9e/ZGphK/LsdlbHfjhoIRzRqWMatdQxFO91egYp5xz50kXxnzu8Hrur8Zo0aGSz65psixj9g+F+Gq7qaROp8PUuyuvw+86asW8jUVIP2VHdpEL76dGY0DH6r9ur0ZpO3Rrobsdem9M5e3QIxfaoValHfpoo3Lt0LKmLjmPL38uwosjojBmQHh1RIOoXrjsO2RoaGjpfH+hoaGVbv706fdZWLw+G9PGJmDZ1FYw6FRIff0YbHapwmMsNhdaNzbgldHeOzdkWcaEd/5GRrYNHz7VDCv+3RqxUVo8OOsozDZXdUXlin26rgCLNxdi2r3RWPZCPAxaEamzz8LmqCzuElrH6/DKyOgK90lsoMW/Rkbj2381xpJn4xAXqcFD755FnrHm4/7pVhsW77Bh2m0BWPZIMAxaAakLTbA55AqP+eEPO2ausWBCXz1WPBqCVg1VSF1oQq7J/b1YHTJ6tdDgkd6GCs/zY7odLywvxrBOOqyaEILPxwVjSHLFBZsvfbrRiMU/GzHtrggse7qBkrZzsiqP955izFyZjwk3hWLFc43QKlaD1I+ykFuSbmabhIc+zIYgAAsej8HnTzWAwyXj0bnZkCT3ee1OGYNSAjDy+urv7LzYp7/YsHinDdNuDcCyccEwaIDUxcWVx/uAHTN/tGDCDXqseCRYSe/FxR7pPWOtBZuPOPDuXQFY9EAQsowSnlha7HGez7Zb8c5GK8b11OO7CcH4bHQQeiZV3unqK59uNmHx1mJMuzMUyyZGK+n9SW7l8d5rwcxvCzFhYDBWPB2tpPcnuaXpDSgdgOPm5uL6ljosmxiFr56Kxn3XB0Ismcr1fKELD87JReMoNb6cGI1Px0Xi6DkHXlxaUM0xvjragEBkHN6PL16d6O+gXLEfDrowc6MTE3qqseJBLVo1EJH6pR25xd7Tek+GhGe+ceDOjiqsfFCLAS1EPL7cgSPZ5e/z6/904fczEmK8XLp2l4xBrVUY2bnyBnlNUbUZAs2AKXBsfRfWeUMgZx2CbuRiICDS6/6CxgA5/xQcm2dBNmV53Ud7yyyomvaC/ZunYZ07ENLfP0N37xIIwQ2qMyqX9OlWKxanWTFtaBCWPRqq3NcWFFV+fe+3YeYPxZjQz4AVE0KV+9oCY+l9LcsoIcso4flBgVj9ZBhmDA/C1iN2vLzCfV8rtslIXVCERmEqfDk+FEseDkGgTkDqgiI4XBV/dnXQJA+FYcg0WDe+iaL3BsKVmY6gh76AEBjldX91sx5w7FsF0yfDYfxwCKTCswhKXQohpGHpPgX/7uCxFX/1FGRJguPAdzUSpx8OODDzRysm3KDDikcC0aqBCqn/8yx7ytpzyolnvrbgzs4arBwfiAGt1Xh8qQVHzrvv2Z9us2PxTjumDdFjWWqgUudZbC6XV57sq8PWZ4JKt1Hd3HUTlwzoNcD93bTo3qzmrnd/5fOypqwwoVVD/93jhJaDIfZ+Ca4d78P5+e1A9iGo7pgPGCK8768xQC48Ddcvb0Au9n5fE+KuhbR/CZxLR8C5YiwgaqC+4zNAXXH9tbpVRzmWGCHiXwPV+PYhLZaM0iIuVMBDX9qRZ1bO2SlexNYndB7biI4qxIcJaN+ods1P/+mGIiz+qQjT7o7AsmcawqATkPph5XV4i01G6zgNXrnLe16pjT79MR+LNxVg2n0xWDY5AQadiNT3zlTeDrVLaB2vxSv3xFzy/Ov3mvD731bEhNWOektdIIhCvd/qq8vuBPzss88QHBxc+v/KNn+RZRmLfszC+NsaoH+XMLRqbMCsR5ogq8CBDXsKKzyud8dQPHVnLG7sGub17yfO2fD7MTOmjklAh2aBaNZIj2ljEmC1y/g+Lb+aYnNlZFnGoo0FGH9zOPqnBKFVvA6zHohBVoELG/Z5rxwBQO/2gXhqaCRu7FRxB8+t1wajR5sAJERr0CJWh8l3RsFklfDnGVt1RKVCsixjUZoV4/vo0b+NFq0aqjFreCCyjBI2HPL+ayAALNhuxYiuOgzvrENSjArTbw2AXgMs32Mv3WdMDz0e7q1HxwTvBYvTJeO1H8x47iYDRl6rQ9MoFZJiVLi5Q/V3AsqyjEU/FWH8wFD07xCAVnFazBoViaxCFzb8Ya7wuAVbjBjRIwjDrwtCUkMNpt8VAb1WxPIdJgDAnuM2nMlzYsZ9kWgVq0WrWC1m3heJA6ft2PGXO22fHByGsX1D0DK2ZjrALpBlGYt22DC+tx79W2vQqqEKs4aVpPfhytLbhhFdtBjeqSS9hxiU9N6rpLfRKmP5XjteuMmA65pp0D5WjRm3B2DvaRf2nXYCAAotEt7dZMWsYQG4NVmLxhEqtGqoQr/W1f8dyLKMRT8XY/yAYPRvb0CrWA1m3ROGrCIXNhywVhzvn00YcV0Ahl8boKT38FDoNQKW/+rOIzO/KcT9PQPxcP9gtGioQbMYNW5OMUCrVgrBLQetUKsEvDIsFM1i1OjQWItpd4Zh3X4rTuY4qz3uVZX+84/45p1p2Lf+nzP674IFvzoxoqMKw5PVSIoSMX2QGno1sHy/9x9ZFu92omczEQ9dp0bzKBET+2jQtqGAJb957n/eKOM/6x14/TYN1F5ua0/21mDstWq0jK4dFSB1t1Q49y2Fa/9XkHP+gv2HlyA7LVB3vMvr/lLmfjg2vQbXwdWQnV7KIrUOqtY3w75pBqTTv0LOPwnH1ncg55+EuvP91RybismyjEXbLBh/gwH925aUYyOCSsoxe4XHLdhWUo510SMpRo3pQwOV+9pvStxbNlBj9r3B6NdGi8aRKlzXXIOnbwzA5sN2OEs6+P7OdqHQIuPJ/gY0i1ahRQM1JvQzIMck42xBxY206qDv9Qhsvy6BffdSSFlHYF75POCwQHvNSK/7m5dOgG3HArgy0yFlH4X560kQBBGapF6l+8imbI9N0/YmOP/eBinvVI3EaUGaDSM6azC8k7ak7NEr9+C93surxTvt6JmkxkPX69A8WoWJ/fRo20iFJb8q+UApA+0Y31vnLgPvMCDLKGPDYc/7caAWiA4WS7cArfu6DtAKmDbEgLu6aMuNdq8u/sznF3yxUxmB+GBP/3WOiZ0fhHTgS8gHlwN5R+Ha+ArgtEBsd6fX/eXzf0D6ZRbkI98DLu/fk2vVQ5APrgDyjgI5h+Fa9wKEkDgIDdpXZ1QqVR3l2K3tVOjRVIWEcBEtokVM7q+GyQb8maXcq7QqAdFB7i3MAGz8y4VhHVS1apFKWZaxaIsR428KRf/kkjr8/VHIKnRiw/6K6/C92xnw1JBw3FjLR/9dUNoOHRxRph3a4PLaobdHVdoOBYDz+U78Z2k2Xn+oIdSq2pO+RP9UVaoNWCwWmM3uG9fJkyfxzjvvYN26dT4LWFVkZNuRXehEj3bBpe8FB6iQ3CwQ+45WfAO6FLtTqVjoNO6vSxQFaDUCfjtS9fP6UkaOE9lFLvRo4y4sgg0qJDfVYd/fFXccXCm7U8aXWwsRbBDROl536QN8KCNfQrZJRo/m7seWgvUCkuPVpZ03F7M7ZaSfdaFHM/cxoiige3NNhcd4czDThfNFMgQBuOODIvSaVYBxi4wev9hXl4xcF7KLJPRo6X5cL9ggIrmJDvuOe++ItTtlpJ+2exwjigK6t9Rj3wl76T6CgNIOIADQaQSIAvCbD/NMVZWmd7OL0jtOVXl6Z3pJ72buPJJ+1gmHCx77NItWITZUKN1n+zEnJBk4XyRj8Owi9HmzEE8tK0ZmYfU3lDPyXMg2SujR0n19BRtEJDfWYt9J740Cu1NGeoYDPVq4j1HSW4d9J5UGaK7Rhd9PORARJGLke9m4fuo5jPogB7/9bfM4j0alHHvBhceEf/u74oYbVY3dJSP9nIweTcuULYKA7oki9p3xntf2nZHQI9Gz6L6+qef+kizj+dUOPNRNjRbRteeRqAqJGoiNOkA6/kuZN2VIx3+BGN+5iudUQxDVwEUdhLLTCjGha9XDepXc5Zj7B4VgvaiUY6cqK8ec6JHk/tFJFAV0T9Ji36mKfxAxWmUE6YTSBlPTaBXCAgR8/Zsy/YPVIWP5bzY0j1YhLqwG84lKA1VcMpx//ex+T5bhOLoV6saXmTYaA6BSQzYXeP2zEBQFTesBsO36/OrDexmUNJK8lz0Z3usJ+0670OOikXnXJ6lK98/Il72XgfGqcuec+4sd3WYZccccE+Zts5XrEKtp/sznAHA0y4kPN5kx684g+K0/SNRAiGkH+fT2Mm/KkE9th9Cok+8+R6t0nsjWAt+d8wpUVzl28Wd8uc+FYB3QOsb7vWrTXxIKLMCw5No1Siwjt6R91srdGR1sEJGcWHEd/p+o4nao/qrboZIk4/nPzuGhgWFoEVuzbU+iuqpKtb6hQ4di0aJFAICCggJce+21ePPNNzF06FB89NFHlzzeZrOhqKjIY7PZr74zJbtQqSREhnqO1okKVSOnoOIKxKU0a6RHbKQGb311FoXFTtidEuZ+dx7n8hzIvorz+lJ2kVKpigzxLPyigtXIKbr673bz/mJ0nngMHZ84hoUbCzB/YizCg2q2oM02KZXayIt+yY4KFJBTweM2+WYZLsnLMUEVH+PN6Txl3w82WTH+Bj0+GhWEEL2A0fONKDBXb8dQdsnjnJHBF6etCjnGCuJd7FLiXe4YETkl50tJ1MGgFfDGtwWw2CWYbRJmrcqHSwKyi2p2VIg3FaZ3kIgck/cGTsXp7T4m26R0dF08N1BkmX1O50uQZeDjrVa8eLMB794ViEKLjAcXmUp/FKguF777yOCL4hAsVngt5xdL3tM7yJ3ep/OUf99fZ8SI6wIxd1wk2sVrMHZOLk5kK/eP61rokGOUMG+zEs9Cs4Q3vy9SwuWHx//runyz8qheZIBnK1W5p3nPZzkmIDKw8v3nprmgEoD7u9auxlBFhIBwCKIacnGOx/tycQ6EwIqnqqiUvRiujN+g6fkEhKAYQBChan8HxLjOyms/yS65Z3u/R12qHLso3YMqzif5xRI+2mLBXde4fwgK0glYlBqC1ftsSJmWh87T87D1iB2fjAmu0ZEVQkAEBJUakinb433ZmA0x+PLSxjD4X5CKzsNx9Gevf9d2uRuyzQTHgR+uOryXI98sK9fyxWlUSf0kxyR7qc+ULasu5JXKz3l/Ny3evNOARWMCcHcXLT7easPr6/3bueDPfG53ynjmSxOeuzkQsf58bNCg3Ndgvui+Zs4FqnpfK0eAqs8USGd2A7l/+eicV6a6yjEA2PyXC53fsKLjf21Y+KsT80dqER7g/V61/HcXejYV0TCkdo0Syy66UIe/uE6n8kn7rLaosB0aokJO4dU9STL3x3yoRAH39wu7qvMQkVuVOgH37NmDXr2URzC+/vprNGzYECdPnsSiRYvw3nvvXfL4GTNmlJtHcMbCQ1ccjtXb89B53O+lW3X98qlRC3jvyWY4cc6Gbo/+gU6pv2PnISN6J4dA9NMgi9U7jeg88Vjp5qzmcqRbKwNWvpyAL56LR692AXhq7jnkFlXv44Grf7eh87/zSzd//rJ9YYq8R/rocVM7LdrHqTFjWCAEKBN7+9Lq3cXo/Nzp0q264h0RpMI7D0Rh8wELOj+fgWsmZ8BokdA2XgN/TJGwer8dnf+voHRz+rEfUpIBhwt4+WYDeiVpkJKgxpt3BuBkroSdJ3yb71f/ZkbnFzNLN6dUPektlXyfd3cPxPBrA9A2XoMXh4aiaYy69JHhFg01mHFPGD77yYROL2ai57RziI9QISpY9EueoCt3IFPC4t1OzBiiqVWPRPmD/ZunAAgwTNwFw+S/oL5mLFzp3wJyzZUlq/fZ0Hl6bulWE/c1k1XCI4uMaB6twuP93aNPrA4ZU1YUo1MTDb4cH4rPHw5BiwZqjF9khLWSualqG90Nj0PbcSiKFz1QbqRn6T5dR8K+d0WFf69LHuihQ7emarRqqMLIa7R4YaAeS361V/sPVmXVpnz+5jozmkercFtK3R8xJPabBiGqBVxrnvZ3UKpFtyYiVj6oxRejtejVTIWnVjm8zjN4rkjGL8clDO/o/x++Vu8yofMzp0q36m6f+cvqnUXo/OTR0q262ioHTlqxeFMBZoxtUO/rNES+VKXl4Mxmc+n8gOvWrcOwYcMgiiKuu+46nDx58pLHv/jii5g0aZLHe9rfH7ricPTtFIrk5oGlr+0lE4/mFjoQE+YeDZhT6ESbJlc3J0j7pgFY9Z/WMJpdcDglRIRocNe0P9G+qX/maujbMRDJTd0LmVyo7OUWuRAT6k7WHKMTbXzw2G6ATkSTGC2axAApzfS46V8n8fX2IjwyqPomrO3bWuuxoqu9pO8l1yQhpswvajnFMtpUMPFzeIAAlYhyE3PnmOQrmhsnOlgpeJJi3J+jVQtIiBCR6eO5lPq2NyC5iXuy89K0NboQE+r+/ByjC23ivM9RFx6oUuJ90eitHKOEqDKjxXq2NmD9Kwbkm1xQiQJCAkT0nJKBhMiaXymybysNkuPcj/JfGBxcLr1NUhXSW0JUyeiC6CABDhdQZJE8RgPmlt3nQnpHuz8nIlBEeIDg+/Rup0dyE/fjT+70lhATUja9pUrSW/Se3iZ3eseEKHFNauCZts1j1MjMLzMPT+cA3No5ADlGFwxaAQKABT8V+yVP1HXhAYBKAHLNnpXnnGK5NC9eLCoI5RpBZff/7bSE3GKg3wfuzg+XDMza5MTC3U5sesz/qwBfTDbnQ5ac5RaFEAKjIBdnV3DUZZy34BRs/7tbeXRUFwyYsqC9433IBTUzRxwA9G2jRXJCWOnr0uvbJJVek0DJfa2CFXrd97WL0t1UPp+YbDJSFxoRqBPw/n3B0JQZ4ffd7zacyXdh6SMhpY/8v3GXGt3+k4eNh+y4JblmOk1kcx5klxNiUDTK3rGE4GhIRu+LIVyg6/0o9Dc8AdPcu+A65/3HY3ViN6hiWqD480d8GOrKhQcIyrV8cRoVV1zXiAoSypdVxWXLKuW4XJOMmOCy+1Rc5wGA5HgVnBKQUSCh2SVW4vWV2pTPdx5z4Mh5F378Vy4Ad59/99fy8UgfA54cUEP1dotyX0PARfe1gEjgKu5rF4g3vAKxaV84v7oXMHlfGbsmVEc5dkGAVkCTCAFNAKTEibhpjg1f/+7CIz0889CK/S6EGYB+Lfw//UXfDgFITnTfSz3qdGXWzlTq8DWzuGB16NsxCMlNPUfgAl7aoUUutEmoetny218W5Bpd6Peie/VrlwTM+joHCzcVYNNrTat8bgL8N18C+VuV7pZJSUlYtWoVTp8+jR9//BEDBw4EAGRlZSEkJOSSx+t0OoSEhHhsOu2VV1SCDCo0aaAr3ZLi9IgOVSPtoLF0H5PFhf1/FyMlKbCSM12+4AAVIkI0OHHOigPHzejX2T+rIQfpL3TKKVtSIy2iQ1RIO+yeq9FkkbD/uA0pzXzf6JNkGfZqHjkQpBPQJFJVuiXFiIgOEpD2t3sklskqY3+GEykJ3iuVWrWAdrEqj2MkScaOvx0VHuNN+1g1tGrgeI67yeJwyTiTLyHWx3MpBelFNInWlG5JDTWIDhGRdsQ9p4bJKmH/SRtSmnovWLVqAe0StB7HSJKMHUesSEksX+kID1IhJEDEjiNW5Jok9G1f8xNpl0vv6ArS+4yr8vRu5CW9j7vzSLtYNTQqIO24e5+/c1w4WyiX7tO55N/jue70LjBLyDfL1ZPeUerSLamBGtHBItLKLM5iskrYf8qOlCbeK4xatYB28Rqk/eWet0+SZOz4y4aUJkrHYVyECjEhIo5neY5kPJHtRGxE+ftvVLAKgToRa/ZZodMIHnMUkm9oVQLaNRSQdsJzPr8dJyWkxHnPZylxItJOenYcbD/h3v+29ip8k6rFyofcW0wQ8FA3FT69u5Y2OCQHpMw/ICZeX+ZNAWLi9ZAy9lz9+R0WwJQF6EOgatYbziM1N39x+XJMVXJfc48gN1klpRxrXFk5pkbaMfcxkiRjxzEHUhq7fxgwWSU89FkRNCrgw1HB0Gk8K/cWhwxR8KzzX3hdTQOQvXM54DqzH+oyi3pAEKBJ6gnnqd0VHqbrMwGG/k/DNP8euM78XuF+2mvuhTPjd7gyD/oy1JVS0kj0KFeUuoYTKfHe67cpCSqkHff84Wb7MVfp/vHhyoIHZc+p1HlcFZ4TAA6fkyAKQGRgzXWI1KZ8/t69wVj1RChWPq5s/75Dqf//b1wI7ruuBn8EkRyQs9IhJHQv86YAIaEH5My9V3Vq8YZXICbdCOfy+4GijKsL51WqjnKsIpKszA9YlizLWPGHC0Pbqzw6g/3Fex1ehbQ/y9ThLRL2n6i4Dv9PcHntUBf2H7deVTv0tutC8M2/GmPlFPcWE6bCQwPD8emTcb6IClG9VKWhHa+88gruvfdePP300+jfvz+6d1cKuHXr1qFTJx9OdnuFBEHA6JtiMOeb80hsoEdctBbvLc9ETJgGA8p01o2d+RcGdAnDqBuVOTmKrS6cOu9ucGdk23HopBmhgWrERimNprW/5iM8WI3YSC2OnLbg/5acQf8uoejZ4dKdnjVBEASM7h+GOWvykRijRVyUGu99m4eYMBUGpLg7QMe+fQYDUgIxqm8YAKDYKuFUtrvClZHjxKHTNoQGioiN0MBskzBnTT76JQciOlSFfJMLn/9UiPMFLgzqUvlKTtUSx+56zNliRWKEiLhwFd7baEFMsIgBbdwVxLGfGTGgjQajSip7Y3voMXlFMdrHqZAcp8bCNCssdmBYZ3eDONsoIcck4VSuUik5ct6FQJ2ERqEiwgJEBOkFjLxGh9mbLGgYKiI2VMT8bUqBPqh99a4YKwgCRvcJwZx1hUiMViMuUo33fihETKgKAzq4f9Ee+/55DEgOwKjeypCBsTcEY/KSXLRvrEVyYx0W/mSExS5hWDd3ui3fYULzhhpEBInYd9yO/1uRjzF9gtGsgTtOZ/OcKDRLyMxX5hk8lKF0NDWOViNQV32NDEEQMPo6Heb8bENipApx4SLe21SS3mVW6R27wKSkdzelMjW2hw6TV5rRPk6N5DgVFqbZlPTupKR3sF7A8E5azFprQahBQJBOwH9+sCAlQVXaCdg0SoX+rdV4bY0F029V9nlrgwXNokR0a1q9I+IEQcDo3oGYs8GIxCg14iJVeG+NETEhKgxo765Ijf0oBwM6GDCqp3J9j+0dhMlL89E+QYPkxhos/LkYFruMYdcGlJ73ob5BmP2jEa1iNWgTp8GqXWb8neXEu2Pc+eh/vxSjU6IGAToR2/+04fXvijDpluBycyjWJrqAQEQ3aV76Oio+EfFtklFckI/8zNN+DNmljb1WjcnfOdC+oYjkWAELd7lgcbgnN39htR0xwQKeuUHJ8/d3VWP0Ejvm73TihiQR3x90IT1Txqs3K/uHBwjl5kxSq5T5lppFutPwbKGMQquMzCJlpOCh88q9r3G4gEBtzTeknDs/hfa2NyFl7od09neor30QgiYAzv1fAQC0t74F2XgOji3/VQ4QNRCiWyj/V2khBDeE0KAtYC+GnK88kSA26w1AgJz7N4SIJtD2fwlS7jG4fv+qxuN3gSAIGH29AXM2W9z3tQ3mknLMXSaNnVeIAW21GNVd+UFm7PV6TF5uUsqxeDUWbrcq13cX5b5nskp4aIERFruM10cEw2STYbIpjeWIQAEqUcD1SVq8vtaMV78txqjuekgyMPdnC1SigG5Na3b1d+vWjxF417twZfwOZ8Ze6HuOAzQBsO9eCgAIuGs2pKJMWNe+BgDQ9XkchoHPofiLxyDlnYYQpNTfZHsxYC+zwqYuCNrkW2H+blqNxgcAxnbXYfJKC9rHqpSyZ4cdFoeMYZ2U7/aFFRbEhAh4ZoByH7+/mxajF5gxf7sNN7RQ4/sDDqSfdeHVW5W/K2WgVikDI8SSMtCGmGABA1or5dDe007sz3ChW1M1ArUC9mW4MGOtFbcmaxBqKLtQhgsOF1BokVFsl3EoU+l8bNOoekYK+jOfN470jFN+yYiz5tGqGi/HpD3zoRr4X8jnD0A+tx9i57GAxgDp4HIAUP5WfB7StjeVA0QNEJnk/n9gAyC6DWAvBgqVEcxi32kQW98K17ePKu9fGGloMwIu/zz+7utyzGyXMWe7E/1aKJ3J+WYZn+9x4bxRxqDWnum746SEjAIZI1L8/yiwN4IgYPQNwZjzYyESY0rq8N8VICZUjQHJZerws89jQLIBo/oo7cpim4RT2e4fADJynTiUYUdogIjYiNr3ZEZpO/SHPCTGaBAXpcF73+SWb4e+lYEBnYIqaYc6PNqh4UGqcnPQq1UCokJUaNawlv6wSfQPUKW7yJ133omePXsiMzMTHTt2LH2/f//+uOOOO3wWuKpIvSUGFpuEVz47hSKzC11aBGLus82h07oL/lNZduQb3TfWA8fNGDPjaOnrmZ+fAQDc3jMCMx9uAgDIKnBg5udnkFvoRHSYGkOvj8Cjt7sf2awNUgeGKXFfkoUis4QuSXrMfSLWY1XjU9kO5JvcvzwfOGnFmLfPlr6e+bUygfHt1wVj5tgGUInA8XN2PJlWhPxiF8ICVejQRI8lz8b5ZYWm1F46WBwyXvnWjCKrjC6N1Zg7Osjjl+BTecqIrQsGd9Air1jC7I1WZJsktGmkwtzRQR6P6CzdZcMHm92/0o2aZwIAvHZHAIZ1VuL53E0GqETgha+LYXXK6BivxoIHghFaA5XK1P7BsNglvPJlHoosEro002Hu+BjPeOc6kV/sTtvBnQORZ5Iw+4dCZBe50CZei7njYxBV5hHTE1lOvP1dAQrNEmIj1Bg/MARjbwj2+Oz31hRi1a/uVbDveF157GTh4zHo1qJ6f1VP7VmS3qvLpPeoQM9457uQb3bHaXB7LfKKZczeZEG2SXlsau79gR7p/eIgA0QBmPilGXanjJ5JGrxyi+fox1l3BGLGWgvGLymGIADXJqox9/6gGvmlObVvECx2Ga98XaCkd1Mt5j4ceVF6uzzTu5NByec/GpX0jtNg7rhIj8e/x/QOgs0hY+Y3hSi0yGjVSI35j0SicZS7KPjjlB2zfyyC2SajWYwa0+8MxdCu/pn24HI1ad8Fz/xvfenru156HQCwfcUiLJw8zl/BuiyD26qQZ5Yxe6sD2cVAmxgBc+/SIqpk0vSzJauSX9A5XsQbt2nwzs9OvP2TE4nhAt4frkHLK1wF+L2tDqz6wz0S4475Suf+wns16Nak5htTrkPfwREYCU2fSRACoyGdPwjb0tFAyWIhQmgsILvDKwQ3gCF1Telrsfsj0HR/BK6TabD9b6Syjy4Ymr4vQAhuCFgL4Ty8Bo4trwNS9c5neympvfTK9b3KpNzXmmgwd2xI5eVYsq6kHLMg26g8Ujl3bHDpfS39rAu/l6xuPvCtAo/P2/BsGOLDVWgWrcJH9wfjg00WjPy4CKKgdALNHRPs8chmTXDs/waWwEjoBz4PMTgarrPpMM2/B7JJSW8xLM4jvXXXjYGg1iHo/nke57GsfwPWDW+UvtZ2vB0AYP99ZfVH4iKD22uUsmezraTsETF3VEBpGp0tlCAI7u+5c2M13hhuwDubbHh7o9LR9/5IA1o2cF9/qddrlbyy2lpSBqowd1RAaV7RqgT8cMCJ97fYYHcB8WEixnTX4oHuno3jh5eYcbbQnZ/u+Fgp0w9Pq74fsv2Vz2sT+cgPkAwRUHWfCAREQ845BNeqhwCz8qgyQmIhoMzItqAYaO77tvSlqmsqVF1TIWXshOvrUcp7He8DAKhHLPH4LOe6FyAfXFG9EaqAr8sxlQgcz5Xx5B925FuAMAPQoZGIJaO05Va8//p3FzrFef7IVdukDghRroUvckvq8HrMfeyiOnyOA/nF7nbVgVN2jHnvfOnrmSvzAQC3XxuImfd7PmJeW6TeFK7E839l2qFPxnm2Q3O8tEPfOlP6euZXJe3Q7sGYObZ2tbOJ6hJBlmtwhuxKyDtH+jsI/mE2Xnqfuig7198h8I+Q4EvvUxcVFPk7BP4RVLs7z6rL+Enb/R0Ev5jzUmt/B8EvLGcK/B0EvzC0qp+PIhXs+tvfQfCLsJSES+9UF2lqdpRobeHMuPp5+/6J1GH1s96C2Ab+DoF/aOvnaDrhhu/8HYRaIe/Zeprvy4h44/yld6qDqjQSsG/fvpWu0LNp06YqB4iIiIiIiIiIiKoH1wWpv6rUCZiSkuLx2uFwYN++fThw4ADGjBnji3ARERERERERERGRj1SpE/Dtt9/2+v60adNgMpmuKkBERERERERERETkWz6dRXXUqFGYP3++L09JREREREREREREV8mnnYBpaWnQ66t3tVAiIiIiIiIiIiK6MlV6HHjYsGEer2VZRmZmJnbv3o1//etfPgkYERERERERERH5mMiVQeqrKnUChoaGerwWRRGtWrXCq6++ioEDB/okYEREREREREREROQbVeoE/Oyzz3wdDiIiIiIiIiIiIqomVeoEvOC3337DoUOHAADt2rVDp06dfBIoIiIiIiIiIiIi8p0qdQJmZWVh5MiR2LJlC8LCwgAABQUF6Nu3L5YuXYro6GhfhpGIiIiIiIiIiIiuQpVWB37iiSdgNBqRnp6OvLw85OXl4cCBAygqKsKTTz7p6zASEREREREREZEPCIJQ77f6qkojAdeuXYsNGzagTZs2pe+1bdsWH3zwARcGISIiIiIiIiIiqmWqNBJQkiRoNJpy72s0GkiSdNWBIiIiIiIiIiIiIt+pUidgv379MHHiRJw9e7b0vTNnzuDpp59G//79fRY4IiIiIiIiIiIiunpV6gR8//33UVRUhMTERDRv3hzNmzdHYmIiioqKMHv2bF+HkYiIiIiIiIiIiK5CleYETEhIwJ49e7Bx40YcOnQIANCmTRsMGDDAp4EjIiIiIiIiIiLfqcfrYtR7VeoEBIBNmzZh06ZNyMrKgiRJ2Lt3Lz7//HMAwPz5830WQCIiIiIiIiIiIro6VeoEnD59Ol599VV07doVjRo1qtfLKxMREREREREREdV2VZoTcM6cOViwYAF27tyJVatWYeXKlR4bERERERERERFRXfHBBx8gMTERer0e3bp1w6+//lrhvnPnzkWvXr0QHh6O8PBwDBgwoNz+Y8eOhSAIHtugQYOqNQ5V6gS02+3o0aOHr8NCRERERERERERUq3z55ZeYNGkSpk6dij179qBjx4646aabkJWV5XX/LVu24J577sHmzZuRlpaGhIQEDBw4EGfOnPHYb9CgQcjMzCzdvvjii2qNR5U6AVNTU0vn/yMiIiIiIiIion8IQeB2hd566y2MGzcODzzwANq2bYs5c+YgICCgwjUxlixZgsceewwpKSlo3bo1Pv30U0iShI0bN3rsp9Pp0LBhw9ItPDy8Skl6uS57TsBJkyaV/l+SJHzyySfYsGEDkpOTodFoPPZ96623fBdCIiIiIiIiIiIiH7HZbLDZbB7v6XQ66HS6cvva7Xb89ttvePHFF0vfE0URAwYMQFpa2mV9ntlshsPhQEREhMf7W7ZsQUxMDMLDw9GvXz/85z//QWRkZBVidHkuuxNw7969Hq9TUlIAAAcOHPB4n4uEEBERERERERFRbTVjxgxMnz7d472pU6di2rRp5fbNycmBy+VCgwYNPN5v0KABDh8+fFmf98ILLyA2NhYDBgwofW/QoEEYNmwYmjZtimPHjuGll17CzTffjLS0NKhUqiuP1GW47E7AzZs3V0sAiIiIiIiIiIiIasqLL77o8cQrAK+jAH1h5syZWLp0KbZs2QK9Xl/6/siRI0v/36FDByQnJ6N58+bYsmUL+vfvXy1hqdKcgERERERERERERP9EOp0OISEhHltFnYBRUVFQqVQ4f/68x/vnz59Hw4YNK/2cN954AzNnzsS6deuQnJxc6b7NmjVDVFQUjh49emWRuQLsBCQiIiIiIiIiqicEUaj325XQarXo0qWLx6IeFxb56N69e4XH/fe//8W///1vrF27Fl27dr3k52RkZCA3NxeNGjW6ovBdCXYCEhERERERERERVWDSpEmYO3cuFi5ciEOHDuHRRx9FcXExHnjgAQDA6NGjPRYOmTVrFv71r39h/vz5SExMxLlz53Du3DmYTCYAgMlkwnPPPYcdO3bgxIkT2LhxI4YOHYqkpCTcdNNN1RaPy54TkIiIiIiIiIiIqL65++67kZ2djVdeeQXnzp1DSkoK1q5dW7pYyKlTpyCK7nF2H330Eex2O+68806P81xYfESlUmH//v1YuHAhCgoKEBsbi4EDB+Lf//53tc1NCLATkIiIiIiIiIiIqFKPP/44Hn/8ca9/27Jli8frEydOVHoug8GAH3/80Uchu3x8HJiIiIiIiIiIiKiO40hAIiIiIiIiIqL64srWxaA6hCMBiYiIiIiIiIiI6jh2AhIREREREREREdVxtedx4JMn/B0C/1Cr/B0C/9Bq/R0C/7Da/B0C/yg0+jsE/hEa7O8Q+MWcl1r7Owh+Mf61w/4Ogl/M+U8nfwfBP2x2f4fAL8Lax/o7CP6hqqf1tXqaz9VxUf4Ogn/Isr9D4B9Zuf4OgX+Ehfg7BETkBxwJSEREREREREREVMexE5CIiIiIiIiIiKiOqz2PAxMRERERERERUbUSBC4PXF9xJCAREREREREREVEdx05AIiIiIiIiIiKiOo6dgERERERERERERHUcOwGJiIiIiIiIiIjqOC4MQkRERERERERUX3A4WL3FpCciIiIiIiIiIqrj2AlIRERERERERERUx7ETkIiIiIiIiIiIqI5jJyAREREREREREVEdx4VBiIiIiIiIiIjqCUEQ/B0E8hOOBCQiIiIiIiIiIqrj2AlIRERERERERERUx7ETkIiIiIiIiIiIqI5jJyAREREREREREVEdx4VBiIiIiIiIiIjqCS4MUn9xJCAREREREREREVEdx05AIiIiIiIiIiKiOo6dgERERERERERERHUcOwGJiIiIiIiIiIjqOC4MQkRERERERERUX3BdkHqLIwGJiIiIiIiIiIjqOHYCEhERERERERER1XHsBCQiIiIiIiIiIqrj2AlIRERERERERERUx1V5YZCNGzdi48aNyMrKgiRJHn+bP3/+VQeMiIiIiIiIiIh8SxC5Mkh9VaVOwOnTp+PVV19F165d0ahRIwgCMxAREREREREREVFtVaVOwDlz5mDBggW4//77fR0eIiIiIiIiIiIi8rEqzQlot9vRo0cPX4eFiIiIiIiIiIiIqkGVOgFTU1Px+eef+zosREREREREREREVA2q9Diw1WrFJ598gg0bNiA5ORkajcbj72+99ZZPAnc5ZFnG7E1WfLXbhiKrjM6N1Zh6WwASI1WVHrdkpxXzfrEhxyShdUMVptwSgOR499fx5S4bvttvx8FMJ4ptwK8vhSLEUL7PdMufDny4xYI/z7mgUwu4JlGND+4L8nk8LybLMmZvMOOrXVYUWWR0bqLB1NuDkBh1iXinWTDvZ0tJvNWYclsgkhPc6ffKShPSjtqRVSQhQCegU2MNnh0UgGYxyneTXyzhuS+N+POcCwVmCZFBIvq10WLSTQEI0lf/YtOyLGP2+mJ89asFRRYJnRO1mHpHMBKjKs/KS7abMe/nYuQYJbRupMaUoSEe8S57/ofnF2DrETveHx2KAe30AIAVuy146asir+fe9q9oRAZVb9z9FW8ASDtqw7s/FuPIOScCtAJu76LHUzcFQa2q/rlAl+yTMG+3jJxioHU0MKWviORGFX/u2iMy3t0m4UwR0CQMeLaXiD7N3Pu3fsvl9bjnegl46BoRO0/LGPOV5HWfr+4V0aFhzcx/KssyZq8pwlc7TMr13VSLqSPCkRhdPu3KWrLViHmbjMgxutA6Vospw8OQ3ERX+vfsIhde/7YA2/+0otgmo2mMGo/cGIKbOgaUO5fdKeOut87j8FkHVj7bAG3itT6PZ7nw/+bEvJ1O5JiA1jECpgzUIDm24mtr7SEX3v3ZiTOFMppECHj2BjX6JHm/B05d68CXe114sb8aY651Xzdztjmx5ZgLh8/L0KiAXZP0Xo+vjVp07YmBqZPQuF0nhDWIxYePjcDvG771d7Aumz/L7zlbLNhyxIHD51zQqATsejmsOqLoPfy77Ji33Y4ck4zWDURMuVmP5LiK47z2oAPvbrbjTIGEJpEinu2vQ58W7viuO+TA0t8cSM90odACrHw4AG0aep7vy9/s+O6AEwczXSi2A78+H4QQfc3O57xktwPz0hzueN+kvUS8nXj3JzvOFJRc3/216JOkxNvhkvHuFgd+OupERoGMIJ2AHk1VmNRPgwbBSlpnFEj4aKsDO064kFMsIyZIwK0d1BjfUwNtDZRfF/grn2fku/DRFit2/O1EjklCTLCIWztqMb6PHlp1zc/l7Y987w+yLGP2RstF6R146fr5Divm/WJ1p/eQwNL0LjBLmL3Jgm1HHcgskBARKKJ/Gw0mDjAguKTunW+W8NxXxfjznBMFZhmRgSL6tdFg0o0BCKqBa71e38/L3tcG6S59X9tiK7mvicp9zSNfO7F0T5l8Pc5QLl/fv8iMXSc966p3d1Zj+i01V3+RZRmzfzTiqx1mpV3SVIupw8OQGH2JdskvxZi3xVRST9Vgyh2hSG7sWb/ce8KOd9YUYf8pB0QBaBOnwacPR0KvUfLx8WwnXl9dhD3H7XC4ZLRqpMGTNwfjuiSdt4+kynBdh3qrSj0X+/fvR0pKCkRRxIEDB7B3797Sbd++fT4OYuU+3WrD4h02TLstAMseCYZBKyB1oQk2h1zhMT/8YcfMNRZM6KvHikdD0KqhCqkLTcg1uW+oVoeMXi00eKS3ocLz/JhuxwvLizGskw6rJoTg83HBGJJc/Q1lAPj0ZwsWb7di2u1BWPZYmBLv+YWVx3u/DTO/L8aE/gFY8XgYWjVSIXV+kUe828Wp8dqdwfh+Ujg+fSAUMoCH5hfBJSnnFUWgf1stPhwdjLXPhGPGnUFIO+bA1FXF1R1lAMCnP5mxeJsZ0+4IxrLHI5R4zyuoPN6/WzHzOyMm9A/Ciicj0aqRBqnz8j3ifcHCX8xe74eDO+qxdUqUx9azpRbXNNNUewcg4L94Hz7rwMPzC9CrlRYrJ0bgrftCsemgDW+uMfkyet7D/6eEmT/JmHCdgBWjRLSKFpC6QkKu2Xuc95yV8cz3Eu5sL2DlKBEDkgQ8/q2EIznu/bc+Inps/zdQgABgYAsl8p1iy+8zor2A+FCgfYNqj3KpTzcasfhnI6aNiMCyp2OU9J6TXXl67zFj5qoCTBgUghXPNkSrOA1S52Qj1+ju+HxhSS6OZznxYWoUvn2+IW5MNuDpBbk4mGEvd77Xvy1ATGjNNah+OOjCzI1OTOipxooHtWjVQETql3bkFleQ3hkSnvnGgTs7qrDyQS0GtBDx+HIHjmSXz9/r/3Th9zMSYrz8PmN3yRjUWoWRnf3feLxS2oBAZBzejy9enejvoFSJP8tvuwsY1F6LkdfUbKPhh3QHZq6zYUIfHVY8HKCEf4kZucXef3zYc9qFZ5ZbcWcnDVY+HIABrdR4/EsLjmS5r2uLA+iSoMKz/SuOi9UB9GquwiM9a6aOcrEf0p2Yud6OCb00WJFqUK7vL6wVX9+nXXhmpQ13pqixcpxBifcyG45kKd+T1QEcPOfCY720WJ5qwOw7dTieK+GxZbbScxzPlSDJwPTBWnz3iAEvDtTiyz0OvL25/P2uOvkrnx/PKYn/0AB890QIXrzZgC932fD2BovP43gp/sr3/vDpVquS3kMDsWx8SEl6Gy+R3jbMXGPGhL4GrHgsFK0aqpG6wFia3llGCVlFEp4fFIDVT4RixrBAbP3LgZdXuuveogD0b63Bh6OCsfapUMwYHqjUz7+tofp5fb2fr7djQm8tVowLUO5rn1sqz9crrLgzRYOV4wIwoJUKjy+zXpSv5cvK1yM6qbH16YDS7bkBNRv3TzebsHhrMabdGYplE6Nh0IpI/SS38vTea8HMbwsxYWAwVjwdjVaxGqR+kutRT917wo5xc3NxfUsdlk2MwldPReO+6wNRdhHb8Z/mwiXJWPhoJJY/HY3WsWo8Oi8P2UXef+gnovKq1HuxefPmCrdNmzb5OowVkmUZi9KsGN9Hj/5ttGjVUI1ZwwORZZSw4ZCjwuMWbLdiRFcdhnfWISlGhem3BkCvAZbvcVcMx/TQ4+HeenRM8N4wdLpkvPaDGc/dZMDIa3VoGqVCUowKN3eo/gq2LMtYtM2C8X0N6N9Wh1aN1Jh1V5AS74MVV24XbLVgxDV6DO+qR1IDNabfHgS9VsDy3dbSfe6+Vo9rmmoQH65Cuzg1nroxAJmFEs7kKwVaqEHEPdcZ0CFeg7hwFbonaXFPNz1+O1Hx9+3TeP9ixvh+gejfTo9WjTSYdVcIsopc2JBuq/C4BVuLMeJaA4ZfY1DifUcw9BoBy3d5VoQPnXXgs5/N+L8RIeXOodcIiA5WlW4qQcDOY3bceU3FlRJf8We8f9hvRatGakwYEIQmUWpc20yLZwcH4/M0M0w275UcX1nwm4wR7QUMby8iKVLA9AEC9Gpg+QHvFYzFe2T0TAQeukZE80gBE68X0TYGWLLPvX90oOCxbTomo1sCkBCm1C60Ks+/h+mBjcdkDGsn1Ngq6LIsY9HPRowfGIL+HQxoFavFrPsikVXowoY/Km68LdhixIjuQRjeLQhJDTWYPiIceq2I5TvdDYB9x+0Y1SsIyU10SIhS49GBoQg2iEg/7Xnf+PmgBdsOW/H80LDqimb58P/qxIiOKgxPViMpSsT0QWolvfd7r9Qt3u1Ez2YiHrpOjeZRIib20aBtQwFLfvPc/7xRxn/WO/D6bRqovdzOn+ytwdhr1WgZ/c/7NTT95x/xzTvTsG/9P2f03wX+LL8B4Mn+BoztoUfLBjXb+bsgzY4RnTUYnqJBUrQK02/RKfflvd7jvHinHT2TVHiohxbNo1WY2FeHto1ELNnl3n9osgYT+ujQvVnFIzDGXKfFwz116Bjvn87uBTsdGNFJXRJvEdMHa5V476sg3rsc6NlchYe6a5Xr+watEu/dyv7BegHz7zPg5rZqNIsUkRKvwr8GaZGeKeFsoVI29WquxozbdOjZXI2EcBH9Wqrx4HUarD9ccw1Ff+bzXi00mDEsED2TNEiIUKFfGy0e7KnH+oPVX1+7mL/yfU2TZRmLtlsx/oYy6X3nhfSupH6+rSS9u5Sk920l6f2bUsdr2UCN2fcGo19rLRpHqnBdcw2evjEAmw874HQpdZxQg4h7uunRIU6t1M+ba0rq586aiXd9vJ/vcGBEJ437vnYhX+/z/p0v/tVRJl+LFefr3lp0b1p5XAwaAdFBYukWpKvZ0c2Lfi7G+AHB6N/egFaxGsy6J0xplxywVnjcgp9NGHFdAIZfG6DUU4eHKt/Xr+bSfWZ+U4j7ewbi4f7BaNFQg2YxatycYigdvZxvcuFkjgvj+gWhVawGidFqTLolBBa7jL/OVX9eJ6orqn8IUzXKyJeQbZLRo7m7AhCsF5Acr8a+095vBHanjPSzLvQoU2kQRQHdm2sqPMabg5kunC+SIQjAHR8UodesAoxbZMSR89VfuczIl5BtlNEjyd3hGKwXkZygxr5T3gtbJd5O9EhyP05YGu9T3uNttstY8ZsV8eEiGoZ6zyrni1xYn27DNU0rf0zRFzLyXMg2SujRoky8DSKSEzTYd8p75crulJF+xulxjCgK6J6k9fiuLHYZz35RiFduD0Z08KUrEav2WKDXCLipQ/UPvfdnvO1OQHfRY0N6DWBzAukZ1VfY2l0y0s8DPZq4P1sUBHRvImBfpvdOwH2Zssf+AHB9ooB9Z73vn1Ms46fjwPD2FVecNh0DCqzAsHY1V7nKyHUhu0hCj5buvBVsEJHcRId9J7x3+tqdMtIz7OjR0v1LsCgK6N7S85iUplr8sNeMgmIXJEnG93vMsDtlXFvmEYocowv/+jIfs0a5H72obnaXjPRzMno0dd9nREFA90QR+85472zed0ZCj0TP+9L1TT33l2QZz6924KFuarSI/kcXd3WOP8tvf7G7ZKRnSuhRpnEnCgK6N1VhX0YF+TzDhR5NPTs5rm+uxr6Mf86Ihwrjnaiq+PrO8NwfAK5vVvH3BABGKyAAlT7mbLQCoYYavJ/XsnxutMo1Gn+gfuV7d3q768TBevHy0ru5l/p5JelttEoI0gkVTs1yvkjC+nQ7rmla/Z2ktS2f14TK87X3fKrk6yu7r1Vk9QEHrnvDhFvnmPHmRhsslYzA87XSdkmZOmewQURyYy32naykXZLhQI8WXuqpJ5V2Sa7Rhd9PORARJGLke9m4fuo5jPogB7/97a7HhgWKaBqtxje7LTDbJDhdMr5MMyMySES7+OpvixLVFVUuGXbv3o1ly5bh1KlTsNs9L/gVK1ZUeqzNZoPN5tmY1Tok6DRX1kjLNik3vIsfx4wKFJDj5ZFHAMg3y3BJXo4JEnA85/IrF6fzlPN/sMmKF242IC5MxGfbrBg934i1E0MQFlB9Dc5so/LZ5eMgIsdYUbwl7/EOFnE827Pj8PM0C95YWwyzHWgarcL8h0LLzR8z6YsibDpkh9UB9G2jxX+GVf88iL6PtzvfzlhtRKcmGvRvd3mdest3WTAkRV8jnST+jHfPllos+sWM7/ZZcHOyHjlGCR9uLC4JV/VVxvMtgEsGIi+aqi4qADie5/2YnGLv++eYve+/6qCMQI37UWBvlh+Q0LMJ0DC45hpNF77XyIs6ZaOCReRU8KhDfnFJepc7RoXj592V6XfGROHphbm47uWzUIuAXitg9oNRaFIy16Asy3hxSR5GXh+IDo21yMitmYp4vvlCent+z1GBAo7nes/jOSYgMrD8/jkmd0V4bpoLKgG4v+s/71Hfus6f5be/5JtlJZ8Hlo9zReHPMcmIDKo8n9d27nhfFI+gyq5v2fv1XcFjdjanjDc22XFLO1WFI2JO5kn4324Hnu9fc49E16Z8fjLXhf/tsOL5QeXngK1O9SnfZ5sqq695D7s7vS++PkQcz/H+w35+sYSPNltxl5fHXyd9acKmwyX189Ya/Of2wKpE5YrUpnxeU0rztZd8ejznCu5rQSJyiq+srjWkvQaxoQJiggQcyZLwxkY7TuRKmH1X9T+dBADZRSX5PLh8G+OK66lBIo5nKe2S03nKse+vM+L5W0PRJlaDb34zY+ycXKx+LgaJ0WoIgoDPxkdiwmd56PLyOYgCEBEkYu64SIRWY9ubqK6pUifg0qVLMXr0aNx0001Yt24dBg4ciCNHjuD8+fO44447Lnn8jBkzMH36dI/3XrkzDtNGxFd63OrfbZj6rbtFP2dU9Xc8VaRkijw80kePm9opFcoZwwLR5/VCrE13+HReitV7rZi6yj0H25wxoT47tze3dtKhRwstso0S5m+14KnPi/DF+DDoynR4vTgkCI/3l3Aix4W3fjRj5vfFmHq7b9Nj9V4Lpq4wlr6e80CYT89/waaDVuw8ZseKiRGXtf/ek3Ycy3Jh1t3VU9jWpnj3bKnDc4ODMG2FES98WQStCni0fxB2H3dA/IdPJrv8gIwhbYRyIx0vOGeU8ctJ4O1bqrdSsXp3MaYuyy99PefhqGr7rHfXFMJokfDZY9EIDxSx4Q8Lnl6Qg/89GYNWsVos/tmEYpuEhweUfzT8n+ZApoTFu51Y/oCuxh7lporVpvKb6haHS8ZTy5UflqcN9l73Ol8kYdwXVgxqo8ZdnatvtEhtzefniySMW2TCoPZa3NW1ds2h90+2ep/NY869OfcHV/tnmqwyHllsRPMYFR7vV74e+uLgADzez6DUz9dbMHONGVNv821HYG3N5/XF3WXuYa0aqBAdJGDs/6w4lSehcYTv66yrfzNj6teFpa/npF5ee+lKSSV9p3d3D8Twa5UfK9rGhyLtLxuW/2rGM7eEQJZlvLqiEJFBIpZMiIROI+DrnWY8Oj8XXz0VjZgQ/uh7JVg9rr+q1An42muv4e2338aECRMQHByMd999F02bNsUjjzyCRo0aXfL4F198EZMmTfJ4T7t6wCWP69ta67FilL3kh5PcklXPLsgplitcISw8QIBKRLnFEXJMMqKuYIGH6JKRQUkx7s/RqgUkRIjILPDtXGl922qRnBBe+tpeMv9HrklCTEiZeJsktGnkPUnDA0Tv8TZKiLrol5xgvYhgPZAYpULHBDW6vZqL9el2DElxVxyjg0VEB4toFqNGaICI+z4uxKP9AjzCc7X6ttV5rGTrkd5lbvI5JgltYqsSb+UcO446cCrPhWunZXvs8+TiQnRpasbiRzwLu69/taBNrBrtq2nYeW2L9wO9AzG2VwCyjBJCDSLO5Lnw1loTEiKqr6ANNwAqAci9aBRfjhmIqqAuGxVYwf5eBj3szpBxPB94e0jFpd+KdBlheqBf8ysM/BXq296A5CbukSml6W10eSzMkWOU0CbOe54LDyxJ74tGZ+YYXYgquSZP5TixZKsJq19oiBaNlPO0jtPit79t+PwXE6bfFYGdf9mw74Qdyc9meJznzrfOY0iXAMy6L/Kq4+s1/AEX0ttzpEROsYyoIO9pFBWEcosKlN3/t9MScouBfh+4R527ZGDWJicW7nZi02P/nFWA64LaVH77S3iAoOTzYglAmWu7uOLwRwUJyDVd/nVRG7njfVE8TJVd34L36/ui0WQOl4ynV9hwtlDGglF6r6MAzxsljP6fFZ3iVXj1luodBVgb8/n5Igmj5xvRKUGNV2+r2VGAQN3O933baJGcUDa9y9TPgy+un18qvS++PqRy34/JJiN1oRGBWgHv3xsEjZdHgZX6OdAsWoVQg4D7PjXi0b4Gj/BcrdqYz2taab6+gnzq9b5mkhAVeHX5+sJqxCfzq6cTsG87/UX11JJ8bryoXVKVeqrJ3S650IZMauDZtmkeo0ZmvnLcjr/s2HLQil//0xBBJStjt4vXYvuR81i1y4yH+1d/RzxRXVClO8WxY8dwyy23AAC0Wi2Ki4shCAKefvppfPLJJ5c8XqfTISQkxGO7nEeBg3QCmkSqSrekGBHRQQLS/nYPozZZZezPcCIlwXvniFYtoF2syuMYSZKx429Hhcd40z5WDa0aHkPWHS4ZZ/IlxIb59gYcpBPRJEpVuiXFqBAdLCDtmPuxTpNVwv7TTqQ09n7zVeKtRtox96MFkiRjxzEHUhpXHm8Z7o5Hby6Miqxsn6pQ4q0u3ZIaqBAdLCLt6MXxdiClsfeKvVYtoF2c2uMYSZKx46i99Lsa1zcA3zwViZUT3RsATL41GDNGeI66LLZJWLPfhuHVuCBIbYy3IAhoEKKCXiPg+9+taBQqom1c9c0zo1UJaNcASDvlzlOSLGPHKRkpjbxXllIaCR77A8D2kzJSYsvv//UBGe0aAK0rWAxClmWsSJcxtK3gtZLtS0F6EU2iNaVbUkM1okNEpP3lnlzZZJWw/6QNKYneR3Bo1QLaxWuR9pe7w0uSZOw44j7GYlcq2uJF0REFofQafnl4GFY93wArn1O2jx+OBgC8NSYST99SfSOQtSoB7RoKSDvhOZ/fjpMSUuK8309T4kSknfRsPGw/4d7/tvYqfJOqxcqH3FtMEPBQNxU+vds/K6TWZ7Wp/PYXrUpAu0Yi0o676w2SLGPHcRdS4ivI5/EqpB33fFRs+99OpPhpgY+qqDDeJ1wVX9/xItJOeDYWt1/0PV3oADyZJ+Gz+/QID/DSAVgkYfRiK9o1FPHardpqH8Fe2/L5hQ7AdrEqvDYsAOLFBUANqMv5vnx6K6Oyyta1Lz+9L6qfX5TeJquMhxYUQaMCPhwV7PF0TkVK6+dOX9fPa1c+94fSfH3CW772nk+VfF35fa0qDp9X6kIx1dRJHqS/uF2iVtolZeqcJquE/afsSGlSSbskXoO0vy5ql/xlQ0oTpV0SF6FCTIiI41me1/6JbCdiSwYeXJj78OJbuSC48zsRXVqV7rLh4eEwGpXHFePi4nDgwAF06NABBQUFMJsrmICrGgiCgNHd9ZizxYrECBFx4Sq8t9GCmGARA9q4O8PGfmbEgDYajLpOGfkxtocek1cUo32cCslxaixMs8JiB4Z1dt+4so0SckwSTpXMV3PkvAuBOgmNQkWEBYgI0gsYeY0OszdZ0DBURGyoiPnblEb7oPbVOzGpIAgYfb0BczZZkBipQlyECu+tNyvxbuuOw9hPCzGgrRajeigdVmN7GTD5KyPax6mRnKDGwm1WWOwyhnVRvpfTeS78sN+G61toEREo4FyhhLk/WaBTC+jTSjnvT4ftyDFJ6BCvRoBOwNHzLry+phidm6gRH169lTNBEDC6ZwDmbCpGYpRKSe91xYgJUWFAO3fnyNhP8jGgvQ6jegSUxDsQk5cVon28BsnxGiz8xQyLQ8awrkq8lRV/y39ebJiI+ItGu6353QaXJOO2TjU3isjf8Z73UzF6tlQaT+sPWDF3SzHevi8UqmpuTIztImDyWhntG0hIbihg4R4ZFod7kY4X1kiICQKe6aVUnu7vLGD0Mhnzd0u4oZmA7w8ri4u8eqNnOE02GT8ekfFCn4rDv+M0kFEIjOhQ8w0mQRAwuncw5qwrQmK0Rrm+fyhCTKgKAzq4O5/HfpCFAckGjOqlJOLYG4Ix+fNctE/QIrmxFgt/MsJilzCsmzJ0slkDDZpEqTF1WT6eHxqKsEAVNvxhxvYjVswZpzyCHBvuWSQEaJWKWONINRqGVW+lfOy1akz+zoH2DUUkxwpYuMulpHeykhdfWG1HTLCAZ25Q7q/3d1Vj9BI75u904oYkEd8fdCE9U8arNyv7hwcI5ToF1Cplvp5mke4K99lCGYVWGZlFykjBQyWV6cbhAgK1tWvUycV0AYGIbuIeqhoVn4j4NskoLshHfuZpP4bs0vxZfgPA2QIJhRYJmYUSXJKMQ5kleT1ChcBqXGVxbHctJq+yon2sCsmxIhbudCj35RQlzi+sUr6DZ/or9/b7u2kxeqEZ89PsuKGFCt8fcCL9rIRXh7jLoAKLjMxCCVklc45dmGcvKkhZPRJQ5irLMck4lVf2OxGU76QGFooY202Dyd/a0L6RiOQ4lTveHUvi/Y1Nub77Kel4/zUajF5sxfwdDtyQpML36SXxLnnc1+GSMXG5DQczJcwZqYNLlkvnJQs1CNCqhNIOwNhQAS8M0CLPLEP5WROl30t182c+P18kYfQ8I2LDRLwwKAB5xWXi78NRYZfDX/m+pgmCgNE9StI7UoW4cLFMepepn88vUurnF9L7ej0mLy9G+1g1kuPVWLi9JL27KN/HhQ5AiwN4/d5AmGwyTDYl3hGBAlSigJ/+tCPHJKNDvAoBWgFHs1x4fa0ZnRvXUP28Pt7Pr9Ng8jcl97VYFRb+ai+5ryn1pRdWWZX72oV8fa0GoxdZSvK1Gt+nO5R8fYu7Ln+pfH0qT8J3B5zo3UKFMIOAI+clzFhvQ9fGIlrV0OrISj01EHM2GJEYpUZcpArvrTEq7ZL27mt07Ec5GNDBgFE9lXro2N5BmLw0H+0TNEhurMHCn4uVdmjJo7+CIOChvkGY/aMRrWI1aBOnwapdZvyd5cS7Y5R9OjXRIMQgYvIXBZgwUOkM/2pHMc7kuXBDWz7hQXS5qtSq6927N9avX48OHTpgxIgRmDhxIjZt2oT169ejf//+vg5jpVJ76WBxyHjlWzOKrDK6NFZj7uggj1/ITuVJyC/zmNngDlrkFUuYvdGK7JIh+nNHB3kMP1+6y4YPNrtH4oyap8zJ99odARjWWblZP3eTASoReOHrYlidMjrGq7HggWCEGqq/8pHa2wCLXcYrK01KvJtoMPeBUM9457qQb3aPlBmcrEOeScLsDWZkG5VHh+c+EFL6OLBWDfx23IFF2ywossiIDBLRNVGDLx4NLZ24V6cBvtplxczvXbA7ZTQMFTGwvQ7j+tTMZLSpfQKUeC83osgqoUuiFnMfDLsovZ3IL3ZXOgZ31Cvpvc6kxDtWjbkPhpcOP78SX++y4Mb2eoTUQBqX5c94//ynDXM2FcPulNG6kQYfjA5D79bVP6fQ4FYi8swSZm+XkW2W0SYamDtMLH1s4qxR9pjrrXOsgDcGi3hnm4S3t8lIDAPev01EyyjPCuD3f8qQAdzSuuKK4dd/yOgUCzSL8E8nUGr/YCW9v8xDkUVCl2Y6zH0k2jO9c5zIL/MYzeDOAcgrdmH2mkJkF7nQJk6LuY9El6a3RiXg40ei8ObqQjw6Nwdmu4zGUWrMvDcCfdrWzPVbmcFtVcgzy5i91YHsYqBNjIC5d2nd6V2yGvsFneNFvHGbBu/87MTbPzmRGC7g/eEatLzCVYDf2+rAqj/c3+Md85VfqRfeq0G3JrVr1MnFmrTvgmf+t7709V0vvQ4A2L5iERZOHuevYF02f5bf722yYNVe94iEOz5Ufthc+GAQulXjaveD22mQVyxj9hYbsk0y2jQQMffegNLwny2UIQju/Ng5QYU3hunxzmYb3t4kIzFCxPt3G9CyzFQkm/504qVv3fGdtFz5/4TeWjxxgxLfpbsd+OBnd3xHLbQAAF67TV/aEVOdBrdTK9f3Tw5kF9uVeN+jL31s7myhBEFwp2HnBBXeuF2Hd7bY8fZmOxIjBLx/lw4tY5R9zhtlbDqijKi5fa7V47MWjtKjW6IK2467cDJfxsl8GX3es3jsc3hK9S+WcIG/8vm2ow6czJNwMk9Cn9fd83kBwOF/h6Mm+Svf+0NqL71Sfn9T7E7vMcHl07vMIjeDO+iU72ejxZ3eY4JLv5/0s078XrLi7MC3PdNywzOhiA9XKZ0hu62YuaZM/bytFuN610zHSL29n5tlzP7JXiZfG9z5usjLfe2Okny92a7k67v0nvn6iBMvfeseYTdphfL/Cb01eKKPDhoVsP24U+lwtAONQgUMbK3Go71q9gmH1L5BSj7/ukCppzbVYu7DkeXbocXukY+DOxmU9P7RWFJP1WDuuEiPdsmY3kGwOWTM/KYQhRYZrRqpMf+RSDSOUroswoNUmPtwBN75wYgxH+XA6QKSGqrxwQMRaB3L1YGJLpcgy/IVD57Ny8uD1WpFbGwsJEnCf//7X2zfvh0tWrTAlClTEB5+5ZULedl1V3xMnaCu3Y3MaiPW03jXV+dz/B0C/2gc6+8Q+Mf57EvvUweNf+2wv4PgF3P+08nfQfAPR82snl3rSL6d9/gfQ1dPpxGor/lcW087FK68WVg32L2vxFznhf3zF4KrCmHIZn8HoVawzGrq7yD4neGF4/4Ogl9UaSRgRIR7oQRRFDF58mSfBYiIiIiIiIiIiIh8q8qTPLlcLqxatQqHDh0CALRr1w633XYbVCqO8CIiIiIiIiIiIqpNqtQJePToUdxyyy3IyMhAq1atAAAzZsxAQkICvv/+ezRv3vwSZyAiIiIiIiIiIqKaUqXVDZ588kk0a9YMp0+fxp49e7Bnzx6cOnUKTZs2xZNPPunrMBIREREREREREdFVqNJIwJ9++gk7duzwmBswMjISM2fOxPXXX++zwBEREREREREREdHVq1InoE6ng9FoLPe+yWSCVltPV08jIiIiIiIiIqrlBEHwdxDIT6r0OPCQIUPw8MMPY+fOnZBlGbIsY8eOHRg/fjxuu+02X4eRiIiIiIiIiIiIrkKVOgHfe+89NG/eHN27d4der4der0ePHj2QlJSEd955x8dBJCIiIiIiIiIioqtRpceBw8LC8M033+Do0aM4dOgQAKBNmzZISkryaeCIiIiIiIiIiIjo6l12J+CkSZMq/fvmzZtL///WW29VPURERERERERERETkU5fdCbh3797L2o8TTBIRERERERER1U7stqm/LrsTsOxIPyIiIiIiIiIiIvrnqNLCIERERERERERERPTPwU5AIiIiIiIiIiKiOo6dgERERERERERERHXcZc8JSERERERERERE/3BcGaTe4khAIiIiIiIiIiKiOo6dgERERERERERERHUcOwGJiIiIiIiIiIjqOHYCEhERERERERER1XFcGISIiIiIiIiIqJ4QRC4MUl9xJCAREREREREREVEdx05AIiIiIiIiIiKiOo6dgERERERERERERHUcOwGJiIiIiIiIiIjqOC4MQkRERERERERUX3BdkHqLIwGJiIiIiIiIiIjqOHYCEhERERERERER1XHsBCQiIiIiIiIiIqrj2AlIRERERERERERUx3FhECIiIiIiIiKiekIQuDJIfcWRgERERERERERERHUcOwGJiIiIiIiIiIjquNrzOLDN7u8Q+EdwhL9D4B9Wq79D4B/BQf4OgX+YDP4OgX8Um/0dAr+wnCnwdxD8Ys5/Ovk7CH4xfspefwfBL+a81sXfQfCLsxsO+TsIfhHbv7W/g+AfsuzvEPhHi47+DoF/GM/6OwR+UfDdr/4Ogl+EDYnzdxCIyA84EpCIiIiIiIiIiKiOqz0jAYmIiIiIiIiIqHqJXBikvuJIQCIiIiIiIiIiojqOnYBERERERERERER1HDsBiYiIiIiIiIiI6jh2AhIREREREREREdVxXBiEiIiIiIiIiKieELguSL3FkYBERERERERERER1HDsBiYiIiIiIiIiI6jh2AhIREREREREREdVx7AQkIiIiIiIiIiKq47gwCBERERERERFRfcGVQeotjgQkIiIiIiIiIiKq49gJSEREREREREREVMdVqROwX79+KCgoKPd+UVER+vXrd7VhIiIiIiIiIiIiIh+qUifgli1bYLfby71vtVqxdevWqw4UERERERERERER+c4VLQyyf//+0v8fPHgQ586dK33tcrmwdu1axMXF+S50REREREREREREdNWuqBMwJSUFgiBAEASvj/0aDAbMnj3bZ4EjIiIiIiIiIiLfEbg6cL11RZ2Ax48fhyzLaNasGX799VdER0eX/k2r1SImJgYqlcrngSQiIiIiIiIiIqKqu6JOwCZNmgAAJEmqlsAQERERERERERGR71VpYZAZM2Zg/vz55d6fP38+Zs2addWBIiIiIiIiIiIiIt+pUifgxx9/jNatW5d7v127dpgzZ85VB4qIiIiIiIiIiIh854oeB77g3LlzaNSoUbn3o6OjkZmZedWBIiIiIiIiIiIi3xOqNByM6oIqJX1CQgK2bdtW7v1t27YhNjb2qgNFREREREREREREvlOlkYDjxo3DU089BYfDgX79+gEANm7ciOeffx7PPPOMTwNIREREREREREREV6dKnYDPPfcccnNz8dhjj8FutwMA9Ho9XnjhBbz44os+DSARERERERERERFdnSp1AgqCgFmzZuFf//oXDh06BIPBgBYtWkCn0/k6fERERERERERERHSVqtQJeEFQUBCuueYaX4WFiIiIiIiIiIiqkyD4OwTkJ1XuBNy9ezeWLVuGU6dOlT4SfMGKFSuuOmBERERERERERETkG1VaHXjp0qXo0aMHDh06hJUrV8LhcCA9PR2bNm1CaGior8NIREREREREREREV6FKnYCvvfYa3n77baxevRparRbvvvsuDh8+jLvuuguNGzf2dRiJiIiIiIiIiIjoKlSpE/DYsWO45ZZbAABarRbFxcUQBAFPP/00PvnkE58GkIiIiIiIiIiIiK5OleYEDA8Ph9FoBADExcXhwIED6NChAwoKCmA2m30awEtZstuBeWkO5JhktG4gYspNWiTHqSrcf+1BJ979yY4zBTKaRAh4tr8WfZKUr8HhkvHuFgd+OupERoGMIJ2AHk1VmNRPgwbBnv2lW/5y4sOtDvyZJUGnBq5prMIHd+mrNa5lybKM2WuK8NUOE4osMjo31WLqiHAkRmsqPW7JViPmbTIix+hC61gtpgwPQ3IT96rO2UUuvP5tAbb/aUWxTUbTGDUeuTEEN3UMAABk5Drx0boi7PjLihyjhJgQEbd2DcT4G0OgVVf/5KKyLGP2ejO+2mVFkUVC50QNpt4ehMSoyrPykjQL5v1kRo5JQutGaky5LQjJCe7v6pUVRqQdtSOrSEKATkCnxho8e3MgmsW4z5t21I531xXjyDkXArTA7V30eGpgINSqmp9UVZZlzP6+AF9tMyrfQzMdpo6MQmJMxem/6y8L5m0oRPppO7ILXXj/4RgM6Bjosc+6fcVYurUI6aftKCyWsHJyLNok+G/V7yW77J7X9yDdpa/vLbaS61tUru8W7jRcd8iJpXscSM90odACrBxnQJuGnue7f5EZu05KHu/d3VmN6bfU8PW90aLkc6uMzk00mHpbIBKjKo47ACzZYcW8rRYlnzdUY8qQgNJ8XmCWMHujBduO2pFZICEiUET/tlpMHGBAsF65v63YY8VLy4u9nnvbi+GIDKrS70ZVpu4yGurrHoYQFA3p/CE41k2FdPZ3r/sKUS2g6fMMxIbtIYYlwL5uOpy75nvupA2Eps8zULW6CUJAFKTz6XCsmwYpc38NxKZisixj9iYrvtptU9K7sRpTbwtAYuQl0nunFfN+sZWktwpTbglAcrw7v3+5y4bv9ttxMNOJYhvw60uhCDF4puGcLRZsOeLA4XMuaFQCdr0cVh1R9KkWXXtiYOokNG7XCWENYvHhYyPw+4Zv/R2sy+av6xsA/shw4s0fi5F+1gUBQId4NZ4bFIDWja5qnbgqCeyTiqCBT0AVEgNHxgEUfPkCHCf2eN1XnzIEwTdPgjq6GaBSw5n1N0wbPoBl55ce+6kbtkTIHdOga3k9IKrgzPwTeR+PgSs/oyai5FVpentc35eZ3r9Y3df3kMDS67vALGH2Jgu2HXW407uNplx6/+e7Yuw55cRf511oHq3Cqsf9N2WPL8tzh0vGu5vt+OmoCxkFkru+3l9brr5eG8iyjNlfHcdXGzNRVOxE51ahmJraEomNAio85uOVJ7H+12z8fdYMvVZEp5aheOa+5mgWW/4YWZbx8Mz92LovD+8/2x4DromuzuhcNlmWMfubXHy1tQBFZgmdkwyYOqoBEhtoKzxm1xEz5q3NQ/pJq1JXnRCLAZ2CPfaZPD8Tq7YXebzXs10APn06oVricSV03R+ArvdjEIOj4co8CPM3L8OVsdfrvtpr74O28wioGrQGALjO7Idl7QyP/cNnnfN6rPn7V2H7+UPfR+Aq1Mf0/qcTuDBIvVWlkrJ3795Yv349AGDEiBGYOHEixo0bh3vuuQf9+/f3aQAr80O6EzPX2zGhlwYrUg1o1UBE6hdW5BbLXvffc9qFZ1bacGeKGivHGTCglRqPL7PhSJbS4Lc6gIPnXHislxbLUw2YfacOx3MlPLbM5nGeHw858cI3NgzrqMaqcQZ8PsaAIe1rtgL96UYjFv9sxLQREVj2dAwMWgGpc7Jhc3iPOwD8sMeMmasKMGFQCFY82xCt4jRInZONXKOrdJ8XluTieJYTH6ZG4dvnG+LGZAOeXpCLgxnK4i/HsxyQZBnT74rAdy80xIt3hOPLbSa8/X1htccZAD79yYLF2y2YdnsQlk0Ih0EjIHV+YeXx/t2Kmd+ZMGFAIFY8EY5WjdRInVeIXJO7o6ddnBqv3RmM7ydF4NMHQyEDeGheIVySct7DZ514+LNC9Gqpxconw/HWvSHYdNCON9d67zCpbp+uL8TiLUWYNjISy56LhUErIvX9c7A5pAqPsdhltI7X4pW7IivexyahS3M9nh0aUR3BviI/pDuU67u3FivGBSjX9+cW5BZ7j+Oe0y48s8KKO1M0WDkuAANaqfD4MiuOZLnzt8Uho0uCCs/2r7xjc0QnNbY+HVC6PTegZjtCP91qxeI0K6YNDcKyR0Nh0ACpC4oqz+f7bZj5QzEm9DNgxYRQtGqoQuoCY2k+zzJKyDJKeH5QIFY/GYYZw4Ow9YgdL69w5+HBHXTYOjncY+vZQoNrmqprvANQ1WYINAOmwLH1XVjnDYGcdQi6kYuBAO/5V9AYIOefgmPzLMimLK/7aG+ZBVXTXrB/8zSscwdC+vtn6O5dAiG4QXVG5ZI+3WrD4h02TLstAMseCVbu5wtNlaf3H3bMXGPBhL56rHg0REnvhSaP+5rVIaNXCw0e6W2o8Dx2FzCovRYjr/FfZ/+V0gYEIuPwfnzx6kR/B6VK/HV9F9tkpC4oQqMwFb4cH4olD4cgUCcgdUERHK6KP7s6GLrcgdA7/wPjd7OQ9doNcGQcQNQTyyEGR3ndXzLnw7jmTWT/dyCy/t0T5rQlCB/9PnRt+5Xuo4pKRPSza+A8/xdy3hqCrH/3hPGHNyA7rTUVLa8+3WpVru+hgVg2PqTk+jZe4vq2YeYaMyb0NWDFY6Fo1VBdPr2LJDw/KACrnwjFjGGB2PqXAy+vLF8nGd5Zh8EdKm6E1wRfl+dKfV3CY700WJ4agNkj9Ep9/Uv/pnVFPv32FBavOYNpqS2x7P+6wKBXIfW132Gzuyo8ZtehAtx7Uxy+/E8XzH85BU6XhNT/2weztfwxC3/IQG1syn+6Ng+LN+Zj2qgGWPZSYxh0IlLfzqi8rmqT0DpBh1fuq7xc7tU+EFvfbF66vflwrK+Df8U0yUNhGDIN1o1voui9gXBlpiPooS8gBHq/r6mb9YBj3yqYPhkO44dDIBWeRVDqUgghDUv3Kfh3B4+t+KunIEsSHAe+q6loXbb6lt5E/2RVatW9//77GDlyJADg5ZdfxqRJk3D+/HkMHz4c8+bN82kAK7NgpwMjOqkxPEWDpGgR0wdrodcIWL7P4XX/xbsc6NlchYe6a9E8SsTEG7Ro20jEkt3K/sF6AfPvM+Dmtmo0ixSREq/CvwZpkZ4p4WyhcgNzSjJeW2fHc/21GNlFg6aRIpKiRdzctuY6AWVZxqKfjRg/MAT9OxjQKlaLWfdFIqvQhQ1/WCo8bsEWI0Z0D8LwbkFIaqjB9BHh0GtFLN/prjTuO27HqF5BSG6iQ0KUGo8ODEWwQUT6aaUTsFcbA2bcG4merfVIiFKjX3sDHuwXjPX7q38EqCzLWLTNgvH9AtC/nQ6tGqkx6+5gZBVJ2HDQVuFxC36xYMS1egzvqkdSAzWm3x4EvVbA8t3uyuLd3Qy4ppkW8REqtIvT4KmBgcgslHAmX0n3H/Zb0aqRGhMGBKJJlArXNtPi2ZsD8XmaBSZbxYVbdZBlGYs2F2H8oDD07xiIVnFazBoTraT/7xWnQ+92AXjq1gjcmBJY4T5DuwVjwuBwdG9dc6PeKrJghwMjOmnc1/ctupLr2+l1/8W/OtAzSYWHemjRPFrExL465fre5b4fDE3WYEJvLbo3rXwEhkEjIDpILN2CdDVXvS7N5zcY0L+tFq0aqjFrRBCyjBI2HLJXeNyCbVaM6KrD8C56JMWoMX1oIPQaYPlvyrXRsoEas+8NRr82WjSOVOG65ho8fWMANh+2w1nSAaDXCIgOFks3lQjs/NuBO7vUfH5Qd0uFc99SuPZ/BTnnL9h/eAmy0wJ1x7u87i9l7odj02twHVwN2enlfqDWQdX6Ztg3zYB0+lfI+Sfh2PoO5PyTUHe+v5pjUzFZlrEozYrxffTo36YkvYcHlqS397IMABZsL0nvzjokxagw/dYAJb33uPPImB56PNxbj44JFef3J/sbMLaHHi0bVH5N1CbpP/+Ib96Zhn3r/zmj/y7w5/X9d7YLhRYZT/Y3oFm0Ci0aqDGhnwE5JhlnC2q2HAsa8BiKty2COe1zODP/RMHnkyA7zAjoMcrr/vYj22Dd9z2c547AlXMCxZs+huNMOrTNryvdJ2Tov2A9sB5FK6bCcfoPuHJOwLp/DSRjTk1FqxxZlrFouxXjbyhzfd8ZeAXpXXJ93xbgPb1bX5zejtL0BoApQwJx33V6xIf7d3Scr8vzYL2A+aMMuLmdBs2iSurrN+s86uu1hSzLWPRDBsYPa4L+10SjVZMgzJrQBln5dmzYVXHe/PSljhh2QyO0SAhE68QgzHisDc7m2JD+t9Fjv0MnjPjsu9P4v0dbV3dUrogsy1i0IR/jh0Sif6dgtErQY9aDDZFV4MSGvaYKj+vdIQhP3RGNGzsHV7gPAGjVAqJD1aVbaKD/yzB9r0dg+3UJ7LuXQso6AvPK5wGHBdprRnrd37x0Amw7FsCVmQ4p+yjMX0+CIIjQJPUq3Uc2ZXtsmrY3wfn3Nkh5p2oqWpelPqY30T9ZlWoFERERiI1VeuBFUcTkyZPx7bff4s0330R4eLhPA1gRu0tGeqaEHmUa86IgoHuiCvvOeK8A7Mvw3B8Arm+mwr6MiisMRisgAAjRK50ABzMlnDfKEATgjrkW9HrHjHFfWEtHE9aEjFwXsosk9GjpbpgHG0QkN9Fh3wnvnWF2p4z0DDt6tHSP9hBFAd1beh6T0lSLH/aaUVDsgiTJ+H6PGXanjGuTKh4lYrTICA2o/gpmRp6EbKOEHknuX7SD9SKSEzTYd9J7RdLulJF+xulxjCgK6J6kwb6T3hvYZruMFbutiI8Q0TBULDkPoLuon1evEWBzAukZ3j+7umTkOpFd5EKPVhelf6IO+45X3Bn6T1Lh9d1UhX0Z3n8535fhuuLruyKrDzhw3Rsm3DrHjDc32mCpZMSGr2XkS8g2yejR3P1od7BeRHK8GvtOVZLPz3rL51rsO1VxR5LRqkx7UNEj7av22qDXCLipfQ2PIhE1EBt1gHT8lzJvypCO/wIxvnMVz6mGIKqBizoIZacVYkLXqof1KrnT232DCdYLSnqfriy9XejRzH2MKAro3lxT4TFUO/jz+m4arUJYgICvf7PB7pRhdchY/psNzaNViAurwU4ilQaaximwHdrifk+WYTv0E7TNrrmsU+ha9Ya6QRLsR7crbwgC9B1uhDPrKCKf+BoN/3sE0S+sh77jYN+H/wpUmt6Xur7LHHM517fRKlV6P/eXmirPjVbZo75eW2RkWZFdYEePDu72UXCAGslJwdj3V1ElR3oympW0Dw1y3/ctNheefe8gXnmwBaLDatdo7owcB7ILXejRxv34cnCACsnN9Nh3rOIBC5fr1z/N6PH0UQx6+W9MW3wO+aaKR1XWCJUGqrhkOP/62f2eLMNxdCvUjS+zjqExACo1ZHOB1z8LQVHQtB4A267Prz68Plbv0pvoH65Kw9dUKhUyMzMRExPj8X5ubi5iYmLgclV+YdpsNthsng0xrUOCTnP5ldB8swyXDEQGehb2UUECjud6ryTkmOTy+wcKyKngcQSbU8Ybm+y4pZ2qdCTQ6QKlM+CDnx144UYt4sIEfLbDgdGLLVj7WADCDNVf+cgueXw3MtizghQVLCKnyPt3n18swSV5O0aF4+fdlcp3xkTh6YW5uO7ls1CLgF4rYPaDUWhSwVyDJ7Md+N9WI54fGnYVMbo82SWPwUQGXZzmInJM3tMw31wS74seZYwKEnE827Px9HmaBW+sMcFsVxpL8x8KK53nsGdLLRZts+C7fVbcnKxDjlHChxuVUXfZxpr91Tm7JI0jQ8qnZUXp/09Ten1fnNaBAo7nXMH1HSQip/jKOkWGtNcgNlRATJCAI1kS3thox4lcCbPvqviRSl+6kJ+85dmK87lcks+93A+zvXdg5hdL+GiLBXddU/Eov+W7bRiSrIywrklCQDgEUQ252HOUhFycAzGyedVOai+GK+M3aHo+AXvOX5CLc6BqNxRiXGfI+SeuPtBVlG1S0qdcegcKl5HeF+cRAcdz6sY9oK7y5/UdpBOw6P/Zu+/wKIr/D+DvvX6XXglJgFBDDQFUes8PERERBCyIIFFArGDBCli+gCKKWFAEKWJBKYIKSgdpKlVQRJROIIW0u1zf/f2x4S5HLgHCXQ6T9+t57oFcdjczO7Ozu5+dnUkPxSOfFeLDjfJNWZ0oBT4ZHlqpgSNFcBQEpQpiQZbH987CLGjjGpa5nqALRdzUQxDUWkB0Iu+Lp1yBREVIDBS6EATf/AQKVr6OguWToG2WhshRi5D99m2w/b3dn1kqk/u6xUt5F5ZRdmWWtwLHsr0HfXNNIj7caMHg6/C1/so4n1sdEqavt+HW5qpK7bl/JbLy5B6fUWGeD9OiwzTIziu7N2hJoijhfwuOonVyGBrVDnZ9P2XBUbRqFIae18kYgCVl5V+8VvW81YwOVSE7/9rOU52bB6FX6xAkRKtxKsuGt5dl46F3TuPL52tDqQhM+QuGSLldM3q2a1JhFpQxDa5oG/o+L0EsOA/70S1ef69pMwSS1Qj7wR+uOb2+Vt3Km+i/rkJBQEnyfuFitVqh0Vy+x8iUKVMwefJkj+9e7h+HSQNqViQ5fmF3SnhiqRyonNTHfVFVPEQcRnVS4+Ym8u6bcpsCXd8twpo/HLirTfkTc1TEqt9MmLgk1/Xz7Ie8jy3hCzNX56PQLOLTh2MQEaTAut/NeHJ+Nj57LBbJ8Z5lez7PgQc/ykbvVAMGtw8uY4sVt2qvBROXu197mD3cvwNa39ZKiw4N1cgqEDFvqxlPfF6AL0aHQ6sW0KmRBk/3CcKk5UY8u6QQGiUwpmcQfjtuh7/PP6t+MWLiF+5gyOyHAzt+WVU3pLX7GE6uoURMsIDhn1lw8oKI2pG+7y2zap8VE791vyoxe1ioz//GpYwWEaMWFqJ+jBKP9PQe3Nx70o5/spyYNsj3x3ag2L59Apq+b0L/+K+QRAfEcwfhPLQSipotKi0Nq/ZbMXGl+7X92UOrzv6l0q6n49til/DiMhNa1VHjrSE6OEUJ8362YPTCQnz9cFilB/uvlmQtRObrXaDQBkHbuCvC7nwdjuzjsB3ZBghy22zZvxqm9R8CAOynD0JT7yYEdXmg0oKAq/ZZMXGle4iV2feV/4qbLxgtEkYtKkT9WCUe6VE5D6uuJ3anhCe+kYd3KXm9Hiirtp7DxDlHXD/PnnDt55dX5h3B36dM+HxyK9d3G37Lxq5DuVg2LXA92UtatbMAExe5J7GY/Vii3/7WrTe529HkRC2SE7X4v+eO4Ze/itC+SdlD3lzPtN0egabl7TB+NKDUGwuuZW64C7a9y8r8fWVieVcRDKJWW1cVBHz33XcByDPJfPLJJwgOdt+8OJ1ObNmyBY0bX35Miueeew7jxo3z+E7zTZerSQoiDAKUAkpNApJtlBAd7L1CRwcLpZc3SYgO8ryxtzslPLnMirP5EuYP1Xk8VYwp3naDaPc6GpWAWuEKZBT455XB7s31SKnjDsDZih+E5hQ6ERvm7g2WXSiiSYL3IGREkDy+V8lJQOR1nIgOlfNyMtuBxVuNWPVsHBrWlLfTOEGD3f9a8fnPRkwe7J4s4ny+E8Pez0KrJA1eGeyfV8C7N9UgpZb7b9qKx7nJMUqILXEflW0U0aSMmQ0jDMX5vqSHRbZRRPQlT+ZDdAqE6BRIigZa1laj7eRsrD1kRd9UuSfFiM4GDO+kR2ahiDC9AmdynZixxoRakf4dl6J7igEpSQmun22O4v1Q4ERsmDvf2YVONEkM7ODfvuI6vo1ejterOb6NIqKDru0Ed3H2whO5/gkCdm+iQUqtcNfPrvI1yrNvX1R+PReK6/nl20OjVUL6gkIEaQW8d28I1GX0APrmNyua1FSieULlzxoqFeVCEh2lBtMWgqIhmbLKWOsKtpt3EtbPhsiv3GhDAGMmNHe8Bymv8sbW6d5Y4zGDr6s9N4qILTGrZbZJKjVz9UXu8r60XZNKtWsUWNfT8f3dfivO5Drx5ahQKIov/KcPVqHtaxew/k8bbk2pnACKaMyB5HRAEerZe0kZEgNngfdJfQAAkgRn1jE4IQf4VHGNEHLzk8g5sq14m3Y4Mg57rOI4dwSaBu28b88P5PIueXyXKO+QS8v7cse3l/PZJce3q7w1At67J7jM9jyQ/Hk+tzslPLnUIl+v36e/LnoBdr8hGikN3ReptuLhRHLybYiNcB9j2fk2NEm6fJD4lXlHsGlPDj6b1ApxUe6evTsP5uLkeTNuGvGzx/KPvXUQbZqEY9HEVpduyq+6pwYjpW6S62f3taoDseElrlULHGhSy7dtTa0YDSKClTiRaUf7Jj7d9BWTii7I7VpwDEreaQkhMRALy2nXAGi7jIGu26MwzhkM57k/vS6jSmoLZWxDmD4f5cNUV1x1L2+i/7qrurt7++23Acg9AWfPng2l0n0Bo9FokJSUhNmzZ192O1qtFlqtZ4MgXcWrwACgUQpoVlOBHcecSEuWsyFKEnYed+LeG7xnKzVRgR3Hnbi/rTtQtv2YE6mJ7r99MQB44oKIBUP1iDB4XlA0r6mARgkcyxHRprbStc6ZfBHxYf65WQ7WKRCsc6dRkiTEhCqw42+LK+hjtIg4cMKKuzt671WiUQlolqjBjr+tSEuRx2sQRQk7j1hxb2d5HbNNvqG89KGAQhBcPSABuQfgsPez0CxRg//dE+m6mfC1YK0CwSWqiSRJiAlRYMdRG5rEy/vaaBFx4JQdd7fz/kqjRiWgWYIKO47akNZM3pgoSth51I57O5T/xFyC+wb9IkEQUKP4Ndzv91lRM0yBpn4OkngvfyV2/GVxnViNZhEHjltxd2f/9zqoDK7j+7gTaY1LHN/HnLj3Ru+B7tREJXYcc+L+tu7vLj2+K+Lwefm4iC3jZuVaBWsFBGvdbakkSYgJFrDjX7tnPT/twN1ty6nn8Srs+MeOtKZymyCKEnb+Y8e9JY4No0XEyPmF0KiAD4aGQFtGzx+TVcLq360Y18vg9fd+J9ohZvwORVJHOI/8VPylAEVSRzh+W3Dt27eb5Y8uFMp6XWDbMOXat3mFyi5vhysIZLRIcnmX8WqfXN5K7PjX4Vne/9pxbxl1hALjejq+zXYJCgEQSnx98WfRP88wvXPaYT+5D9rGXWHZX/xamyBA27gLjJs+ufLtCAr51eDibdqO74WqhufrxKoa9eHMOeWjhF9emeX9j7308X3T5Y5ve7nHt9EiYeSCAmiUQrnteaD563x+MQB44oKEBfeVvl4PlGC9CsF693WhJEmICddgx++5rqCfsciBA0cLcff/JZS1GUiShFc//RvrfsnCwomtkBjrec36YP/auLOH5xtU/Z7+FRPub4gebaJ8mKMrI1+ruh9ES5KEmDAldvxZhCa15XprNDtx4F8L7u4W7tO/fe6CHXkmzwfjlc5ph/PMAagadIb9jzXyd4IAdYNOsGyfV+Zq2q5joe/xOArn3gXnmf1lLqe58R44Tu+HM+MPX6e8Qqp9eRP9x13V0XPs2DEAQPfu3bFs2bJKmwSkLMPbqjFhpRXNayqQkqDEgl12mO0SBrSULyqe/daK2BAB43vIjdR9N6oxbJEF83ba0a2BEt8fcuDQWRGvFL8+YHdKeHypFX9kiJh9lxZOSXKN1xSmF6BRCgjWCrirjQqzttgRFyogPlyBeTvkMVp6N6mcxkgQBAzrEoLZPxUgKUaNhEgl3v2hALFhSqS1cF8kDH8/E2kpegwtDgwN7xaCCZ/noHktDVJqa7BgcyHMNhED2spdqevVUKNOtAoTl+TimdvDEB6kxLrfi7D9iAWzH5R75JzPc2DYe1mIj1Ti2dvDcKFET5SYUP/2iBMEAcM66jF7QxGSopVyvn8yITZUgbSm7ovp4XPykNZMi6HFQb7hnfSY8HUhmieqkVJLhQU/m2G2SRhQPOPpqRwnfjhgRceGakQGK3AuX8ScTUXQqgV0bew+wc3dXIROyRooBGDtQSvmbC7C2/eEVvp4FIIgYFj3UMxek4ekWBUSotR497tcufxbuoM2w2dmIK1lEIZ2k59ImywiTpYYB/F0jgN/nrIiLEiJ+Ei57uaZnMi44EBm8fgdxzLl5aNDlYip5JPt8HZqTPi2+PiOV2LBL7bi41tOx7MrLPLx3VMu+/tuUmPYQjPm7bChW0MVvj9kl4/vW911I88sISNfRGbxWEwXxw+NDpZnAz55QcR3Bx3o0lCJcL2AI+dFTFlrxQ21FUiupNlTXfV8oxlJUUokRCjw7roixIYokNbEXR+Hz81HWlMNhrYvrucddZiw1IjmCUqkJKqwYLuluJ4XB4qLAwRmm4Q3B4XAaJVgtMr7ITJI8KjHq3+3wikC/VID92qVY9cn0PR7C2LGAYhn90N10wMQ1AY4DnwNANDcNgNS4TnYN70hr6BQQ4gpDgAoNRBC4iDUaArYTJByT8iL1OsCQICU8y+EyDrQ9HweYs4/cO7/OgA5lAmCgGHtdZi9yYKkSAUSIpR4d725uLzdN8jDPy1EWhM1hhYHfYZ30GHCMpNc3gkqLNhhgdkGDGjtriNZhSKyjSJOFtfzI+edCNKKqBmmQHjxZE5n80Tkm0Vk5ItwihL+zJCffNSOVCLoOuhV443WEISYOu6xIaMTk5DYJAWmvFzkZlRewKciAnl8d2ygwZtrivDKShOGttdBlIA5W8xQKgS0rev7oUzKY1z3ASKGfwD7ib2wHd+D4B5jIGiCULR9MQAgYviHcOZloGDFKwCA4JufhP3kXjiyjkFQaaFr/n8wtBuCvM/Hu7e59l1Eps+D9eh2WP/aCl2zNOha9Eb2jNsqNW8lCYKAYR2Kj++L5e06vkuU97wCubwvHt8ddZiw1ITm8aoS5Y0S5S1h5PwCmO3Am/cEldmen8hxosgmIdsoweJwH9/1Y5SuMY8rg6/P53anhMe/seCPcyJmD9F5vV6/XgiCgGF9EjF7+Qkk1TQgIVaHd786htgIDdJudPd2H/7qXqTdGIOhveXXKl+ZewTfbcvE+083R5Beiaw8+RXQEIMKOo0SMeFar5OBxEdrSwUMA0EQBAxLi8Ds73OQVEODhGg13l2RjdhwFdJauTssDJ9+CmmtgzG0h3xPabKIOJnpHivxdJYdf560yNeqUWqYLCLeX5WNXq1DEB2mwqksG978Ogu1Y9Xo1CxADy6LWbZ+hKDBM+E8vR+O03uh6/QgoDbA9tuXAADD4FkQCzJgWfM/AIC26yPQ93oapi8ehnjhFIRguXe0ZDMBNvewIdAGQ5NyG4q+m1TZWbpi1bG8if7LKnRXv3HjRl+no0L6NFPhQpGEWZvtyDLZ0KSGAnPu1rleLzibL0IQ3E8NW9dSYnp/Ld7ZZMPbG21IihTw3mAtGsXKy5wvlLDhiBz86D/H4vG3FgzVoW2SHAR4uqcGSoUNz660wmIHWiYoMH+oHmGVMCnIRek9Q2C2SXj5qwsoMItoU0+LOaNiPJ4En8x2ILdEkK5PawMumJyYtTofWQVONEnQYM6oGEQXTxaiVgr4aFQ03lqVjzFzslFkk1A7WoWp90Sia1P5gmLbX1acyHbgRLYDXSdleKTp8Du1/J/vrno538sKUWCR0CZJjTkjwjzzneNEbonJXvq01OGCScKstSZkFYpoEq/CnAfCEF38ao5GDew+bsfCbUUoMEuIClbghrpqfDEm3GMw7y1/2TB7ozxbcuOaKrw/LBRdkgMTJEn/vzB5P3yeI5d/fS3mjI3zmFznZLYDuSb3SwkHT1px/0z3+B1Tl14AAPRvG4ypw+QLjw0HivD8Z+7xB8fNk1+9HNsnHI/eWrlB/z7N1MXHtw1ZRkk+vu/Ru16HOlvg5fi+Q4d3NlqLj28F3husQ6NYd/BuwxEHnl/pHktl3DL5/2O7qPFoVy3USmD7MYd8g2IDaoYJ6NVYhTGdK/c16/TOOrl8Vxjlel5HjTnDQz3r+QURuUXu7jt9UrS4YBIxa71Zruc1VZgzPMS1vw6ddWJ/8cySvWbkefy9dU+FIzHCvZ++2W3F/zXTIFQfuFdLnX9+B3tQFNRdx0EIioF4/g9YvxwGFE8WIoTFA5L7OBdCakCfvtr1s6L9KKjbj4LzxA5YP7tLXkYbAnX3ZyGExAGWfDgOr4Z905uAGNgZddM7a2G2S3h5ZZFc3rVVmDMsuPzybqEpLm8LsopfLZwzLNjjdcEvf7Xi/Y3uc9nQufLYdP+7w4ABreW2690NZqzY674Av+MDeRzWBQ8EV3pg6ErVad4G4z9b6/p58PNvAgC2L1uIBRMeDFSyrligju96MUp8eF8I3t9gxl0fFUAhQK4394d4vJpcGcy7l0MREo2Q256HMjQW9tO/I3vWnRAL5XOOMjIRUsnjW2tA+N3ToQyPh2S3wH7ub+TOGwXz7uWuZSz7vkfe5+MQ0vtJhA+eCvv5o7jw8TDY/tlZqXm7lKu8vzW5j+/7Q0qXd8nrlhZa+bplvdl9fN9fsrwd2F88s26vt/M9/t668WGu9vzF5Sb8etzdvt3xfkGpZSqDr8/nntfrnjOPLrhPh7ZJ11cPofR+tWG2OvHyx3+hoMiBNslhmPNcS2g17jI4ed6C3EL3g9ov1p4FAAybvM9jW/8b0xgDul0/Y6iXJ713JMxWCS8vPIeCIhFtGuox54lEz2vVLBtySwxVdPC4BfdPdz/MmbpEbhP6dwjF1AdqQqkA/jptxYrtBSgsciImXIWOzYLw+O3R0FzlW2W+Zj/wLcxBUdD1egaKkBg4zx6Ccd7dkIzydYsiPMHjukXb7n4IKi2C75vrsR3z2umwrJvu+lnTsj8AwLZ/Oa5n1a28if7LBKmsWT7K4XQ6MX/+fKxfvx6ZmZkQRc9xiTZs2HDVCZEWtb7qdaqE6MjLL1MVWSyXX6YqCqmmkwCcq/g4bv9pusAPUh4I5r/OBDoJAaFv+N+4MfO10S/uDXQSAmL2/9oEOgkBcXad9zGrqrr4npcf87pKsl7ZDLZVTvMbA52CwCg8G+gUBETed78EOgkBEd73pkAnISCEzisDnYTrgvRJ80AnIeCE9IOBTkJAVOhR2eOPP4758+fj1ltvRfPmzSEI10+3eyIiIiIiIiIiIvJUoSDgl19+iSVLlqBPnz6+Tg8RERERERERERH5WIVeptdoNGjQoIGv00JERERERERERER+UKEg4Pjx4zFz5kxUYDhBIiIiIiIiIiIiqmQVeh34559/xsaNG7F69Wo0a9YMarXnDILLli3zSeKIiIiIiIiIiMiHOK9DtVWhIGB4eDjuuOMOX6eFiIiIiIiIiIiI/KBCQcBPP/3U1+kgIiIiIiIiIiIiP6lQEPCirKws/PXXXwCA5ORkxMTE+CRRRERERERERERE5DsVmhjEZDLhgQceQM2aNdGlSxd06dIF8fHxGDlyJIqKinydRiIiIiIiIiIiIroGFQoCjhs3Dps3b8aqVauQl5eHvLw8fPvtt9i8eTPGjx/v6zQSEREREREREZEvKAR+qqkKvQ68dOlSfPPNN+jWrZvruz59+kCv12Pw4MH48MMPfZU+IiIiIiIiIiIiukYV6glYVFSEGjVqlPo+NjaWrwMTERERERERERFdZyoUBGzfvj0mTpwIi8Xi+s5sNmPy5Mlo3769zxJHRERERERERERE165CrwO/88476N27NxITE9GyZUsAwP79+6HVavHTTz/5NIFERERERERERER0bSoUBGzRogX+/vtvLF68GIcPHwYA3H333bj33nuh1+t9mkAiIiIiIiIiIvIRofpOjFHdVSgIOGXKFNSoUQMPPvigx/fz5s1DVlYWnn32WZ8kjoiIiIiIiIiIiK5dhcYE/Oijj9C4ceNS3zdr1gyzZ8++5kQRERERERERERGR71QoCHju3DnUrFmz1PcxMTHIyMi45kQRERERERERERGR71QoCFirVi1s27at1Pfbtm1DfHz8NSeKiIiIiIiIiIiIfKdCYwI++OCDeOKJJ2C329GjRw8AwPr16/HMM89g/PjxPk0gERERERERERERXZsKBQGffvpp5OTk4OGHH4bNZgMA6HQ6PPvss3juued8mkAiIiIiIiIiIvIRoUIvhVIVUKEgoCAImDZtGl566SX8+eef0Ov1aNiwIbRara/TR0RERERERERERNeoQkHAi4KDg3HjjTf6Ki1ERERERERERETkB+wDSkREREREREREVMUxCEhERERERERERFTFXdPrwERERERERERE9B+iEAKdAgoQ9gQkIiIiIiIiIiKq4hgEJCIiIiIiIiIiquIYBCQiIiIiIiIiIqriGAQkIiIiIiIiIiKq4jgxCBERERERERFRdSFwYpDqij0BiYiIiIiIiIiIqjgGAYmIiIiIiIiIiKq46+d1YK0m0CkIjOwLgU5BYKjVgU5BYBgzA52CwFBU0+cNdnugUxAQ+uSEQCchMKy2QKcgIGb/r02gkxAQo5/fHegkBMTsV1IDnYTAqKavTVnP5gY6CQGhjf4n0EkIDIs10CkIiLC6EYFOQmCczw50CogoAKrpnTkREREREREREVH1cf30BCQiIiIiIiIiIv+qpj3ciT0BiYiIiIiIiIiIqjwGAYmIiIiIiIiIiKo4BgGJiIiIiIiIiIiqOAYBiYiIiIiIiIiIqjgGAYmIiIiIiIiIqguFwE8FvP/++0hKSoJOp0Pbtm3xyy+/lLv8119/jcaNG0On06FFixb44YcfPH4vSRJefvll1KxZE3q9Hmlpafj7778rlLYrxSAgERERERERERFRGb766iuMGzcOEydOxJ49e9CyZUvcfPPNyMzM9Lr89u3bcffdd2PkyJHYu3cv+vfvj/79++PgwYOuZd544w28++67mD17Nnbt2oWgoCDcfPPNsFgsfssHg4BERERERERERERlmDFjBh588EGMGDECTZs2xezZs2EwGDBv3jyvy8+cORO9e/fG008/jSZNmuDVV19F69at8d577wGQewG+8847ePHFF3H77bcjJSUFCxcuxNmzZ7FixQq/5YNBQCIiIiIiIiIiqjasVisKCgo8Plar1euyNpsNu3fvRlpamus7hUKBtLQ07Nixw+s6O3bs8FgeAG6++WbX8seOHcO5c+c8lgkLC0Pbtm3L3KYvMAhIRERERERERETVxpQpUxAWFubxmTJlitdls7Oz4XQ6UaNGDY/va9SogXPnznld59y5c+Uuf/Hfq9mmL6j8tmUiIiIiIiIiIrq+COwP9txzz2HcuHEe32m12gClpvIwCEhERERERERERNWGVqu94qBfdHQ0lEolzp8/7/H9+fPnERcX53WduLi4cpe/+O/58+dRs2ZNj2VSU1OvNBtXjeFfIiIiIiIiIiIiLzQaDdq0aYP169e7vhNFEevXr0f79u29rtO+fXuP5QFg7dq1ruXr1q2LuLg4j2UKCgqwa9euMrfpC+wJSEREREREREREVIZx48bh/vvvxw033ICbbroJ77zzDkwmE0aMGAEAGDZsGBISElzjCj7++OPo2rUr3nrrLdx666348ssv8dtvv+Hjjz8GAAiCgCeeeAKvvfYaGjZsiLp16+Kll15CfHw8+vfv77d8MAhIRERERERERERUhiFDhiArKwsvv/wyzp07h9TUVKxZs8Y1scfJkyehULhftu3QoQM+//xzvPjii3j++efRsGFDrFixAs2bN3ct88wzz8BkMuGhhx5CXl4eOnXqhDVr1kCn0/ktH4IkSZLftn4VpCXtAp2EwLDaAp2CwFCrA52CwLDbA52CwFBU05EHFEKgUxAY1fX4rq7tuVYT6BQExOjndwc6CQEx+5XUQCchMFTKQKcgIKz/+m92wuuZNqV+oJMQGBZroFMQENK5rEAnISCE6IhAJyEghDu3BzoJ14VqG38pQRi8M9BJCIhqemdORERERERERERUfTAISEREREREREREVMUxCEhERERERERERFTFMQhIRERERERERERUxXF2YCIiIiIiIiKi6kKophMYEnsCEhERERERERERVXUMAhIREREREREREVVxDAISERERERERERFVcQwCEhERERERERERVXEMAhIREREREREREVVxFZod+N9//0W9evV8nRYiIiIiIiIiIvInzg5cbVWoJ2CDBg3QvXt3fPbZZ7BYLL5OExEREREREREREflQhYKAe/bsQUpKCsaNG4e4uDiMGjUKv/zyi6/TRkRERERERERERD5QoSBgamoqZs6cibNnz2LevHnIyMhAp06d0Lx5c8yYMQNZWVm+TicRERERERERERFV0DVNDKJSqTBgwAB8/fXXmDZtGo4ePYqnnnoKtWrVwrBhw5CRkeGrdBIREREREREREVEFVWhikIt+++03zJs3D19++SWCgoLw1FNPYeTIkTh9+jQmT56M22+/3eevCUuShFkbLPj6NysKLBJa11ZhYj8DkqKU5a63eJcFc3+2ItsoonGcEi/eakBKojv7VruEaWvM+P53G+xOCR0bqDHxNgOig91x0h3/2DFzvRlHzjth0Ajon6rBE2l6qJSCaxsTVxXh0BkH/s0W0a2RGu/fG+zT/Lvy85sdc3fYkW2U0LiGAi/erEFKQtn7YM0fDszcbMOZPAl1IgU81VODrg3k/NudEmZusmPzUQdO50kI1groUFeJcT3UqBHizv+Yryw4fF5EjklCmF5A+yQFxvfUeCzja5IkYdZGC77ebXOXd1/9FZS3FXO3W4r3jxIv9tGXLu8fzfj+oF0u7/pqTOyr9yjvxhPzSm33rTsNuLWFBgCw+4QD09ea8W+2CItdQny4AkPaaDC8g843mS+Zn2pQ3ot/tWHudps7j7foLpNHO2ZutOFMnog6UQo81VOLrg3dZSxJEmZtsuHrvXa57tRSYmIfHZKi3Ok/lOHEW+us+P2sEwoF0KuJGhN6aRGkKT1Qbm6RhP4fmXC+UMIvzwQjVOe7wXQX/2LD3G1WOe9xCrx4ix4pieXk/ZAdMzdY3XlP06JrI7Vn3jda8fWeEnnvq/N63NgcEgbPMeHweRHLRwWhSU15mdO5ItJmGkst/+VIA1JrXdOpo0ySJGHWejO+/tUip7uOGhP7BSEp+jLH+04L5m41F7fvKrzY14CUWvL+yCsSMWu9GduO2pCRJyIySIGeTTV4PE2PEJ27Lvx+2oG3fjTh0FknBAAtElV4urcBjWv6J68e6fdx3f/pTzu+3G3HoQwn8s3A8ocMaBLnub2vdtvw3UEH/shwwmSDz+v0laiu5V1RDW/ohF7p41C7WSuE14jHBw8Pwv51KwOdrCsWyPM5ACzba8X8HVYczxERrBXQu6kaL/c1+CWvl+OPug8AL68wYsc/dmQWiDBoBLSqrcJTvYNQL6b87VYGZav7oLrpQSAoBlLmn7CvmwTp3AGvywpRDaHq9CQUcc0hhCXCvv5VOHd/WnrB4BpQd30WinpdAZUeUt4J2Fc/A+nc737OTdkkScKs1fn4eocRBWYJretqMHFQJJJi1eWut3hrIeZuKEB2gRONEzR4cWAEUupoXb8/mW3HGyvysPtfK2wOCZ2b6PHiwAhEh7rLdsycLBw+bUOO0YkwgwLtG+kwvl84aoT5v12TJAmz1prw9S9mFJhFtE7SYOIdIUiKLv9vL95ehLlbTMguFNG4pgov3h7qUafv++gCfv3X7rHOkLZ6TB4QCgA4fNaOjzeZsOe4HbkmEQkRStzVzoBhnSrn2F68T8K83RKyTUDjGOCF7gJS4so+l645IuHd7RLOFAB1woHxnQV0rete3mSTMONnCev/AfLMQGIYMDRVwF0t3ctMXCdix0kg0wgYNECrmvJ26kVW3jmc5+8qQsGJQaqrCt3Nz5gxAy1atECHDh1w9uxZLFy4ECdOnMBrr72GunXronPnzpg/fz727Nnj6/Tik61WLNppxaR+BiwZFQK9RkD6AiOsdqnMdX743Yapq80Y212HZWNCkRynRPoCI3KMomuZKauLsPEvG2beFYSFD4Qgs1DEo1+4b4APZzjw0CIjOjdUY/nDoZgxOAgbDtvx1lqzaxmnBOhUwH3tdWhfz38N0Q+HHJi61oaxndVYlq5Hcg0F0r+wIMfkfR/sOeXE+OVW3JmqwvIH9UhLVuGRJVYcyZTzb7EDf5xz4uHOGixN12PWnVocyxHx8BKrx3baJinx9gAtVo/RY+ZALU7mSXh8qdXbn/SZT362YtEuKybdZsCSB0OgVwPpi0zll/dBG6b+aMbYbjosGxUil/cik2d5rzFj4xE7Zg42YOGIYLm8vzSV2tb/+uux9alQ1yetsfvCRK8B7m2rxWcPBOP7R0IwposOMzdY8NVvvt0n1aG8fzhkx9SfrBjbVYtlDxnkMltchByT6HX5PaecGL/UgjtbqbH8IYOcx6/MOJLpdC3zyXYbFv1iw6RbtVgy0gC9WkD64iJYHfJ+O18o4oFFRagdqcBXI4PwyT0GHM104rlvvU929OIqC5Jr+D4A+sNBO6b+aMHYblosGxWE5BpKpH/mWV898n7SgfHfmHFnazWWjw5CWmMVHvlSfjjhyvs2GxbtsmFSXx2WpAfJ7eSiIq/HzZtrrYgtJ7D76TADto4Pdn2axfvvZvKTrRYs2mHBpNuDsWRMmHy8zy8o/3g/YMXUH0wY20OPZWPD5Lozv9C1/zILRWQWinimdxBWPRaOKQODsfWIDS8scx/vJquE9PkFqBmuxFejw7D4oVAEaQWkzy+A3Vn23/YFf9R9sx1oU0uJp3pqvW4DkNuBzvWVGNVJ4/M8XanqWN7XQmMIwunDB/DFK48HOikVEsjz+afbLXhnvQUPdtLhu7Eh+HRYMDo1KD8o40/+qPsA0Cxehf8NCMb3T4Tjk+GhkACM/LQATjGw9VrR+Faouj8Px7Z3YVtwG8SsP6EZvAAwRHlfQa2HlH8S9s1vQDJmel9GGwrtvV9DEh2wfT0Ctnm94Nj4OmDJ919GrsAn6wuxaEshJg2OxJIna0CvUSB9dmb5ZbvHhKnLczH25jAse7omkuPVSP8wEzmFcrteZBUx8oMsCAIw/5FYfP5EDdidEsbMyYJYomzbNtDi7RHRWP1CPGY+EIOT2Q48Pi/b73kGgE82F2HRtiJMuiMESx6JlK875uaVn+/9Fkz9rhBjewZj2WNRSK6pRvrc3FLXP4Nu0mPri9Guz9N93B0sDp1xICpYgTfuCsN346IwukcQZqwpxGfbi/yWV1f6/5IwbYuEse0ELL1XQHI08OAyCTlF3vO896yEp36QMLC5gGX3CujZAHh0pYQj2e7lp22W8PNx4I3eAr6/X8CwVgJe2yhhwz/uZZrFCni9l/z7OXcIkACkL5Mq9Tjn+Zvov61Cd7Qffvgh7rnnHpw4cQIrVqxA3759oVB4bio2NhZz5871SSIvkiQJC3dYMLqrDj2baJAcp8K0gUHILBSx7k97mevN327BoBu0GNhaiwaxSky+zQCdGli6xwYAKLRIWLrHhmd7G9CunhrNE1SYckcQ9p50Yt8pBwD5Rj05Tomx3fWoE6XETXXVeOpmPT7fZYXRKjc6Bo2ASf2CMPgGLaL92Dtu/i47BrVSYWCqGg1iFJjcRwOdWsDSfd73waJf7ehUX4mR7TWoH63A4900aFpTgcW/ycuH6ATMu1ePW5qqUC9KgdREJV7qrcGhDBFn890n4uFt1UhNVCIhXIHWtZR4qIMa+0+Lfmt0JUnCwp1WjO6iQ8/GaiTHKTFtQHF5Hy6vvK0Y1EaDga2Ky7uvXi7vvSXKe68Nz96sl8s7XoUp/Q3Ye8pd3heF6gTEhChcH63a/cSkaU0V+rbQoGGsEokRSvRrqUGnBmrsPuG5jWtVHcp7/g4bBrVWF+dRicm3auU87i0jj7ts6NRAiZEdNKgfo8Tj3bVyHn+Vl5ckCQt32TG6sxY9k9VIrqHEtP46ZBZKWHdYLp9NRxxQKQW83EeLetEKtEhQYtKtOvz0pwMnLnhegH7xm9xz5YH2vg+YzN9hlfPeSlNcX3VXkHcVRnbUynnvoUPTmkos/sXmzvtOG0Z30bqPmzv0Hnm/aMvfdmz7x4FnepUdLArXex4DaqV/nhpKkoSF28wY3U2Pnk2L2/dBwcXtu63M9eZvK27f2+jQIFaFybcHycf7bjlg3aiGCrPuCUGPJhrUjlKiXX01nvw/AzYetsFRXJf/zXIi3yzhsZ561ItRomENFcb20CPbKOFsnvdgnK/4uu4DwO0paoztqi33YdT97TR4qJMWLcvpcepP1bW8r8WhLT/i23cmYd/a/07vv4sCeT7PN4uYucGCaQMMuC1Fg9qRSiTHKdGjcWCCgP6q+wAw5CYdbqyrRmKEEs0SVHji/wzIyBdxJjew9Vp1w0g4D3wF58FvIOUchePHFwG7GcoWg7wuL507AMemqRAPfwc4ve8TVdvRkAoy4Fj9DKRzByDln4Z4/GdIeSf9mZVySZKEhZsLMLpXGHq2MCA5QYNpQ6OQme/Eut/LDkrN31SIQR2CMbBdMBrEqTF5cCR0GgWW7pQ7I+w5ZsWZCw5MuTcKyfEaJMdrMPXeKBw8ZcPOv93lP7x7KFKTtEiIVKF1XS0eSgvF/hM2vwdHJEnCwp+LMLpHEHo20yG5phrTBocis8CJdYfKfng8f6sJg27SY+CNejSoocLkO0Lk89+vZo/l9GoBMSFK1ye4RK+wgTfq8UK/UNxUT4NaUSr0a63HgBv0WHvQ+wNdX1qwR8Kg5sCAZgIaRAmYlCZApwKWHfS+/MK9EjolASNvEFA/SsDjHRRoEgt8vs9dPnszgNubCriploCEMAGDUwQkxwAHzrmXGZwi4MZE+ffNagh4vIOAjELgTIGfM1yM52+i/74KRar+/vtvPPfcc6hZs2aZy2g0Gtx///0VTpg3p3NFZBkldKjvvrEJ0QlISVSVCt5cZHNIOHTWiQ4lboYUCgHt66td6xw664DdCY/t1otRIj5M4VrG5pCgVXne/OrUAqwOef3KYnNKOJQhokNd942bQhDQPkmJfWe8N377TnsuDwAd6ymx73TZjWWhBRCAMl8PyzNLWHXQgVaJ/gsKuMq73iXlnaAsv7wzvJR3PVXp8q53aXkLpbb7yvdmtJuWj0EfF2LpHiskqewLqT8yHNh7yoEbk3zXC7Q6lHeZeaxbdpr3nXaiQ13P/dyxvgr7TstPzU/nScV1x71NV90pXsbmBNRK+W9dpCsO8u4+6e5VdTTLiQ+22DCtvw6Cj6u63D6J3uvraafXdfadcnrkCwA6NnDn63Su5P24SVR6bDPbKOKllRZMu0Pvyrc3D39RhA5vFOKeuSZsKOdm/Vq523f3jXmITiG37yfLa98d6NDAHZxVKAS0b6DBvpNlp7XQIr8Gf3Eoh7oxSoQbBHyzW37NymKXsHS3FfVj5CC4v/ij7v9XVMfyrs4CeT7f/o8DogScL5DQZ1YBur6VjyeWmJCRH5gbxsqq+0U2Cct2W5EYoUBcWADrtUINIa45xOPbSnwpQTyxDYr4VhXfbIOeEM//DnW/96Ad+ws096+CMmXItaf3GpzOcSKrQESHRu5hYUL0CqTU0WLfMe/BMJtDwqFTNo91FAoB7RvpsO+4zbWMIACaEvchWrUAhQDs/td7sCvP5MSq3UVolaT123X6RacvOJFVKKJDQ3f9DNErkFJLjX0nvQeFbA4Jh844PNYpq06v2mdGu8mZuG1GNt5aXQizrfygZqFFQpjev3Xe5pRw6DzQvrZ73yoEAe1rA/syvKdvf4bn8gDQqQ6wr8QQ+q1qAhv/lXDeKEGSJOw6JeF4LtCxjvcyLLJLWHZIQmIoEBdy7fm6Ejx/E/33XVO0oqioCCdPnoTN5tnAp6SklLue1WqF1ep5MtTYRWjV5R+8WUa5UY26ZJyX6CAB2WW8OpdbJMEpelknWMCxbPmmKatQgloJhF5ywogKFpBdKG+3U0M1Fu6w4rsDNtzSXI1so4QPNlpc61eW3CIJTgmICvI8GUQHCziW430fZBul0ssHCcgu43Uzq0PC9A023NpMiWCt53rT19uw+Dc7zHagZYICs4f4fvy7i8os72AFso3e93nZ5a3AsWyHa7vey9tzu49116FdPRV0amDbUQcmf2+GyQYMa+fZa6rrW/m4YJL/7iPddBjUpuxeVVerOpS3O4+lj+uLx+ilso0SooK95LG4/Fx1x8t+u7hMuyQlpv0kYe52G+5rq4bZBry13lq8vryvbA4J45dZ8HSaFvFhCpzycW8KV9695KX8vF+6rxQl8i6n0fv+kX8nSRKeW2HGXTdo0CJBidNe8hWkAZ7tpUXr2kooBAE//WHH2C/NeP8u+KUHTVbhxXR7O94v1757OT6yymgjTCI+3GTG4BvddTlYK2Bheige+awQH26UeyDUiVLgk+GhrgtPf/BH3f+vqI7lXZ0F8nx+KleEJAEfbbXg+Vv0CNEKmLnBggcWGvHtmBCPwEpl8Hfd/3ynBdN/NKHIBtSNVmDeiNBKz6MHQwQEhQpSkedrqZIpG4rI+hXerBBeG8rUe+H8dS5sOz+AomYKVD0nQnLaIR5adq2prpCs4td3o0I8H9RFhyhd9xOXyjU55bIttY4CxzLl4EhqkhZ6jYDpK/PwZN8wSBLw1qo8OEUgq8Bzu9NX5mLxViPMNgktkzSY/VCMr7JXpnLrdFn5LhK9H98hChzLct9X9k3VIT5cidhQBY6cc2D6D0Ycz3Ji1rBwr9vdc9yG1fstmD3C++99Jc8sDwMVdcnQg1EG4Fiu93WyTUD0pcsHCcgu8frwi90FvLxOQrc5ElQKQBCAV9Lknn8lfb5fwltbJRTZgboRwNyBAjSVdP7i+Zvov69CQcCsrCwMHz4ca9as8fp7p7P8HglTpkzB5MmTPb57+c4ETBqU6PHdqv1WTFzp7j4/e6h/Jtm4Ep0aqPH0zXpMWmnCs0sBjRIY002P3044qtSYmnanhCeKx32b1Kd0MGtkezUGpqpwNl/C+1tsmLDSitlDtBB80EVq1QEbJq4qUd5+mlTlSj3czX3SaVpTBbNdwrxtllJBwMUPBMNkA/afcuCtdRbUjlKgb4vAjbN1NQJZ3oHWMFaJKbfrMO0nK2ast0KhAO67SY3oIMF1TL+13or60Qr0Swnc2FH+sGiXDSYr8FDnsutpRJACIzq460SLBCUyC+WgqS+CgKv2WTHxW/e4q7OHhV7zNi/HaBExamEh6sco8UhPvet7i13Ci8tMaFVHjbeG6OAUJcz72YLRCwvx9cNh5faUpCvD8q5erqfzuSgBdifwwi161ziAb91pQKc3C7DruAOd/Tw2YGXX/dtSNejQQI2sQhHzfjbjiS8L8cVDYR7DmVQJggDp3O9wbJ0OAHBm/gEhuhFUqffAVklBwFW/mTDxqwuun2eP8k/ALTJYiXdGRGPyklws2lIIhQDc2tqAponqUvcgI3uEYmC7YJy94MD7awow4bMczH4oxqfXbav2mjFxWaHrZ38G3Ia0dUfNkmuqEROixPA5uTiZ40DtKM/b2CPnHBi7MA9j04LQqZHvHshXps/2AfvPAR/0ExAfCvx2Bnh1g4TYIKBDid6AtzUGOtQWkGUCPt0t4cnvJXw+BKXeWvMFnr+rsCpwP0cVU6Eg4BNPPIH8/Hzs2rUL3bp1w/Lly3H+/Hm89tpreOutty67/nPPPYdx48Z5fKdZlVZque6NNR4zwNmKexjnGEWPgeyzTVKpmQ8vijAIUCpQapDZbKPkmjkuJkSA3QkUmEWPp8k5RsljbL8RHXUY3kGLzEJ5ttQzuSJmrDWjVkTldT+OMAhQCig1KYScH+8HcnSwUHp5k4ToS3qf2J0Snlxmxdl8CfOH6kr1Crv49yMMAupGAfWjtej2rhn7zoho5YNxpbonq5GS4O7LbiuOJZcqb6NYgfIWXfsnJris8hbL3IcAkJKowgeb5e7nJZ+qJ0bIaUmuoUSOScJ7Gy0+CwJW5fIu+TfkPIoA3NvNNkmlZne8KDpYQI7RSx5LlDEg77fYEq9HZBslNIlzb/O2Fmrc1kKNbKMIvUaAAGD+TrvrmN513IkjmSJ+/EO+2L34F9u/acSozho81u3aLjJdefeal/Lyfkn9NpWs3/J6OcZL8l6indx1zIl9p51IebXQYzt3fmxC3xQ1pt2hhzcpiUps/9c3wx90b6JBSq1w18+24glbcowiYkMvOd7LmPHNfbxf/vgwWiWkLyhEkFbAe/eGeLwe9d1+K87kOvHlqFAoiu+opg9Woe1rF7D+TxtuTfHPzYQ/6v71iuVdvVxP5/OYEPnfBiVmyI0MUiDCICCjEsaQquy6H6JTIEQHJEUr0bKWXK/X/mFD35YBqtdFuZBEBwRDNEqmXgiKhmTKqvh2jVkQc456fCXl/AOhUe+Kb/MqdW+uR0qdONfPrrItdCI2rESbXuhEkwTvweaIIKVctoWeHSiyC0VEl+gd2KmxHmtf1iPX6IRSISDUoECnF0+j1iWBsIhgJSKClagbq0b9ODW6TTyLfcdtaFXXd+XfvanWYwZfj/uzErMVZxtFNIkvq04rvB/fl+T7Uim15b97ItvpEQQ8et6BEXNyMfgmA8b09P9Dh3A95PP3JUM95hSV7u13UXQQkH3p8ibJtbzFIeGdbRLevU1At3rycZ0cA/yZJeHT3ZJHEDBEKyBECyRFAC1rAu0+kLDuKHBrY1/l0I3nb6Kqp0LRqw0bNmDGjBm44YYboFAoUKdOHQwdOhRvvPEGpkyZctn1tVotQkNDPT7eXgUO1gqoE6V0fRrEKhATLGBHiZtQo0XCgdMOpNby3uhoVAKaxSs91hFFCTv/tbvWaRavgloJj2X+zXLibL5YaruCIKBGqAI6tYDvf7ehZpiApn6cLfNSGqWAZjUV2HHMfbEgShJ2HnciNcF7caYmKrDjuOfFxfZjTqQmupe/GBA6cUHEp/fqEGG4/A3lxUmobD4aErFUeceUUd5nnOWXd00v5X3MUbq8j5Uo72wnzuZLZW4XAA5nOBGmF8p9rUaU5HFCfKUql/dFZebxkjSXlJqo9Cg/ANj+rwOpxcHJxHBBrjsltmm0FtcdLwHM6GAFgjQCVh9yQKtyjy/17iA9VowyYHnx59Xb5N6hnw034N4br733iNw+KTzyIrdPDq/pBIDUWkqPfAHA9n/c+UqMuJj3S9tJ9zIv3KLDitFBWF78+ehe+Qp0xiA9nuxR9gXU4XNOV4D1WpVu35XFx7t7bBijRZTb99rlte8q7PjHvY4oStj5jx2ptd3lY7SIGPlpAdRK4IOhIaV6xJjtEhSC5wPRiz/7c7I9f9T96xXLu3q5ns7nrYv/PZbjPs7yikTkFkmIr4QxpCqz7nsjwbfXJVdNtEM6dxCKOh1KfClAUacDxLN7K77ZM7uhiKjn8Z0QWRdSwZkKb/NqBesUqBOjdn0axKkRE6rAjiPucfqMFhEHTliRWkYQTqMS0KyWxmMdUZSw84gFqUmlHyhHBCsRalBg5xELcowiujf3/tBO3o7878Wgja8EaxWoE61yfRrUUCImRIEdR92v8RotIg6csiO1tveH4hqVgGYJKo91RFHCzqO2cuv04bPyMVAy+PT3OQfu/zgX/dvo8GTvyul1rFEKaFYD2HnKvW9FScLOU0BqTe/XSS1rAjtPepbF9pNAavEQ+w4nYBdRqnen8nLnJunicV6BjFwBnr+Jqp4K9QQ0mUyIjY0FAERERCArKwuNGjVCixYtsGfPHp8msCRBEDCsvQ6zN1mQFKlAQoQS7643IzZEgbQm7gZk+KeFSGuixtB28g378A46TFhmQvMEJVISVFiwwwKzDRjQWj4xhegEDGytwbTVZoTpBQRrBbz2fRFSayk9Lk7n/mxBpwZy1/u1f9gwZ6sFbw8OgrJEa3000wm7U0J+kQSTTcKfGfKFaVlPRipieFs1Jqy0onlNBVISlFiwyw6zXcKAlvI+ePZbK2JDBIzvIefvvhvVGLbIgnk77ejWQInvDzlw6KyIV4pf/7Q7JTy+1Io/MkTMvksLpyS5xu8J08tjTOw/48TvZ0W0qaVAqE7AqVwJMzfbUDtCQKsyblavlSAIGNZOi9lbrEiKUiIhQoF3NxSXd4nXEYfPN8rl3VbOz/AOWkxYXoTmCSp5/+ywyuXdqkR5t9Jg2poS5f2D2aO8N/xlR45RRMtEFbQqeWDxj7ZaPF6PXLzLiprhCtSLlvP/23EH5m234L62vn0KVR3Ke3h7DSassKB5vBIp8Qp3HlOL87hCLvfxPeU83NdWg2ELijBvhw3dGirx/cHiPPaVj3lBEDCsrRqzt1rltiJcwLubbIgNEZDW2H0sfvaLDa1qKWHQCNj+rwNvrrViXE+ta4KU2pGeec0tHrelfoyizElUrj7vWkxYbpbznqDEgp02Oe+tivO+zIzYUAHj03TuvM8vwrztVnRrqML3B+04dNaJV24rkfd2Gvm4iVQUHzdWj7xfeuNr0Mh3CrVLDCC/fJ8NaqWApsU9J3/604Gle+14tZ9/xgEVBAHDOuoxe6PZfbyvKypu3903EcPn5iOtqQZD28s3PsM76jBhqVFu3xNVWLDdArNNwoDisTmNFhEj58sDib85KARGq+Sa0T0ySIBSIaBjAw3eXFOEV1aaMLS9DqIEzNlihlIhoG1d/74q6Ou6D8gT+WTki8gsHq/24vih0cGCq6dollFEtlHCyeKZsI+cdyJIK6BmmALhev/3Kqyu5X0ttIYgxNRxj6EWnZiExCYpMOXlIjfjVABTdnmBPJ/XjVaiZ2MV/rfajMm3ycvMWGdGvWgF2tb13bXZVe0LP9T9Uxec+OF3Gzo2UCMySMC5fBFztpihVQno2iiwQ5Q4fpsLdZ/pEM/9DiljP5Q3jADUBjh//wYAoO4zHZLxPBxb3pRXUKghRDeQ/69UQwipASG2CWArgpR3onib86C592so2z0M8fD3EGq2hDLlLth/eiEQWQRQXLZdQzH7p3wkxaiQEKXCuz/kIzZMibQW7u5hw987j7QUA4Z2kXvLDu8WggmLc9C8tgYptbVYsLkQZpuIAW3dAa2lO42oH6dGZLAC+47Z8PqyXNzfNQT1asjHz/7jVvx+0oY29bQINShwKtuBmT/koXa0yqe9AMvMdycDZm8wISlaKd+f/WRCbKgSac3cf3v4x7lIa67F0A7yvhjeOQgTluSjeaIaKYlqLPi5SD7/3SCfz07mOPDdXgu6NNYi3KDAkXN2TFllxA111UiuKef7yDkHhn98AZ0aaTG8s8E1LqNSEBBZRo96X7m/tYDnfpTQPFZCizh59l+zHbijmfz7Z9eIqBEMjOskp2NYKwHDvpZ79XWtC/zwlzy5yOQ0+ZwbrBVwY6KEN7dK0KmA+FDg19PAt38Az3aVlzmVJ2H1EaBjHSBCD5w3AnN+laBVAV3q+jW7Ljx/E/33VejqJzk5GX/99ReSkpLQsmVLfPTRR0hKSsLs2bPLnTHYF9I7a2G2S3h5ZREKLBLa1FZhzrBgjycFJy+Irpt1AOjTQoMLJhGz1luQZRTRpKYSc4YFe7xu9dwtBigEMx7/0gSbQ0KnBmq8fJtnf+4tR+yYvdkCm0NC4zgl3r8nGF0aeTY4Dy0yekxRfscH8ut2h1+N8Nk+6NNMhQtFEmZttiPLZEOTGgrMuVvn6k59Nl+EILjz1rqWEtP7a/HOJhve3mhDUqSA9wZr0ShWXuZ8oYQNR+STZv85nrOMLRiqQ9skJXRqAWsPOzBriwizTX4Fp3N9JcZ0Uvt1wOn0TsXlvapEeQ8N8izvXCdyi9y9YPo01+CCScKsDWZkGeVXIOfcF+RZ3r31UAjA418Vucv7VvfTVLUC+PwXG6askQedrR2pwLM36zG4jfvkJkrA2+vMOJ0rQqkAakcq8VSaHkNu8O3FdnUo7z7N1HKZbbLKZVZDgTn3GFxldjZfgiC4j6vWtZSYPkCHdzZa8fYGCUmRCrw3RI9Gse56kN5BA7MNePk7S3HdUWLOvQaP8VJ+P+vErM1WFNmAetEKTO6rw+2VPP5fn+bFed9YnPc4BeYMLZn3S8q3tgrTB+rxzgYr3l4vB/reu0uPRjVK5L2jBmabhJdXlcj7UMNVjwn14WYrzubL9btetAIz7tSjdzP/7Z/0zjo53SuMcrrrqDFneGj57XuKtrh9NyOrUH4VZc7wENf+O3TWif3Fs4T2mpHn8ffWPRWOxAgl6sUo8eF9IXh/gxl3fVQAhQD5PHF/iEdvA3/wR93f8JcDz690H9vjlsr/H9tFg0eLX2H/8jc73t/i7oExdIHc1v2vn84VgPS36lje16JO8zYY/9la18+Dn5cDJtuXLcSCCQ8GKllXLFDncwCYdkcQpqwxY/RiEwQBuClJhTn3Bft91tSy+KPua1QCdh+3Y+E2Mwos8gRSNySp8MWosFID+Fc28fD3cOgjoe70JBAUDSnzT9i+Hg4UTxYihMYDkrudE4JjoR3+vetn1U0PQXXTQxBP7oTty3sAANK5A7CvGANVl6eh6vAopPxTcGx4FeIf31Zq3i6V3jMEZpuIl7+6gAKziDb1tJgzOtazbHMcyDW5u231aR2EC0YRs37IR1aBE00SNZgzOhbRJV6tPZ7pwNvf5SG/SER8pAqje4VieDf3K/c6jYC1B4owa3U+zDYRMaFKdG6ix5helTMxTHpXg1ynlxaiwCKiTZIGcx4Iv6ROO5Brcp9f+rTUyXX6J6Ncp+NVmPNAhOt1YLVSwPajNizYVgSzTULNMCV6tdBiTI8g1zZ+/N2CCyYJK/dasHKv+7wXH6HAhgn+nRSlT7KAXDPw7g4J2UVAkxjg4zsERBdPSpdR6Nmrr1W8gDdvAWZul/D2NqBOODCrn4BG0e6F3uoj4O2fJTy9WkK+RQ4EPtFRwF3Fc25qVcBvZyQs3AsUWOSJSG5IBL4YIiDqCt7q8RWev4n+2wRJkq668+xnn30Gh8OB4cOHY/fu3ejduzdycnKg0WiwYMECDBky5KoTIi1pd9XrVAlW2+WXqYrU1fRpjd1++WWqIkU1PTFXpVmDrkZ1Pb6ra3uu/W9MhORro5/fHegkBMTsV1IDnYTAUF3fr9z7i/Xfc4FOQkBoUyo+c/F/msUa6BQEhHTuGsan/A8Ton3XSeW/RLhze6CTcF2QVnQJdBICTui/JdBJCIgK9QQcOnSo6/9t2rTBiRMncPjwYdSuXRvR0dE+SxwRERERERERERFduysOAl46m295ZsyYUaHEEBERERERERERke9dcRBw717PGbz27NkDh8OB5ORkAMCRI0egVCrRpk0b36aQiIiIiIiIiIiIrskVBwE3btzo+v+MGTMQEhKCBQsWICJCHksgNzcXI0aMQOfOnX2fSiIiIiIiIiIiIqqwCo0J+NZbb+Gnn35yBQABICIiAq+99hp69eqF8ePH+yyBRERERERERETkI9V1AkNChabsLCgoQFZW6VmUsrKyUFhYeM2JIiIiIiIiIiIiIt+pUBDwjjvuwIgRI7Bs2TKcPn0ap0+fxtKlSzFy5EgMGDDA12kkIiIiIiIiIiKia1Ch14Fnz56Np556Cvfccw/sdru8IZUKI0eOxJtvvunTBBIREREREREREdG1qVAQ0GAw4IMPPsCbb76Jf/75BwBQv359BAUF+TRxREREREREREREdO0qFAS8KCgoCCkpKb5KCxERERERERER+ZPAiUGqqwqNCUhERERERERERET/HQwCEhERERERERERVXEMAhIREREREREREVVxDAISERERERERERFVcdc0MQgREREREREREf2HcGKQaos9AYmIiIiIiIiIiKo4BgGJiIiIiIiIiIiqOAYBiYiIiIiIiIiIqjgGAYmIiIiIiIiIiKo4TgxCRERERERERFRdKDgxSHXFnoBERERERERERERVHIOAREREREREREREVRyDgERERERERERERFUcg4BERERERERERERVHCcGISIiIiIiIiKqLgT2B6uuWPJERERERERERERVHIOAREREREREREREVRyDgERERERERERERFUcg4BERERERERERERVHIOAREREREREREREVRxnByYiIiIiIiIiqi4EIdApoABhT0AiIiIiIiIiIqIqjkFAIiIiIiIiIiKiKu76eR1Yff0kpVKFhgQ6BYGRVxDoFARGWDUt70JToFMQGE4x0CkIiLx9/wY6CQER3jw+0EkIiLPr/gx0EgJi9iupgU5CQIx+eV+gkxAQsye2CHQSAkLbqmGgkxAYZzMDnYLAUFTP1wOF2MhAJyEwRCnQKSCiAGBPQCIiIiIiIiIioiqumna/IyIiIiIiIiKqhqppz19iT0AiIiIiIiIiIqIqj0FAIiIiIiIiIiKiKo5BQCIiIiIiIiIioiqOQUAiIiIiIiIiIqIqjhODEBERERERERFVFwInBqmu2BOQiIiIiIiIiIioimMQkIiIiIiIiIiIqIpjEJCIiIiIiIiIiKiKYxCQiIiIiIiIiIioiuPEIERERERERERE1QUnBqm22BOQiIiIiIiIiIioimMQkIiIiIiIiIiIqIpjEJCIiIiIiIiIiKiKYxCQiIiIiIiIiIioiuPEIERERERERERE1YWC/cGqK5Y8ERERERERERFRFccgIBERERERERERURVXodeBTSYTpk6divXr1yMzMxOiKHr8/t9///VJ4oiIiIiIiIiIiOjaVSgImJ6ejs2bN+O+++5DzZo1IQiCr9NFREREREREREREPlKhIODq1avx/fffo2PHjr5ODxERERERERER+Qs7clVbFRoTMCIiApGRkb5OCxEREREREREREflBhYKAr776Kl5++WUUFRX5Oj1ERERERERERETkY1f8OnCrVq08xv47evQoatSogaSkJKjVao9l9+zZ47sUEhERERERERER0TW54iBg//79/ZgMIiIiIiIiIiIi8pcrDgJOnDjRn+kgIiIiIiIiIiJ/48Qg1VaFxgSsV68ecnJySn2fl5eHevXqXXOiiIiIiIiIiIiIyHcqFAQ8fvw4nE5nqe+tVitOnz59zYkiIiIiIiIiIiIi37ni14EBYOXKla7///jjjwgLC3P97HQ6sX79etStW9d3qSMiIiIiIiIiIqJrdlVBwIuTgwiCgPvvv9/jd2q1GklJSXjrrbd8ljgiIiIiIiIiIiK6dlcVBBRFEQBQt25d/Prrr4iOjvZLooiIiIiIiIiIyA8UnBikurqqIOBFx44d83U6KkySJMxaW4Svf7WgwCyidZIaE/sHIym6/Kwt3mHG3M1FyDaKaFxThRf7BSOlltrr9h/6NB9bj9jx3n2hSGumBQAcPuvAx5uLsOe4HbkmEQkRStzVVodhnQx+yae3dM1anY+vdxhRYJbQuq4GEwdFIim2dB5KWry1EHM3FCC7wInGCRq8ODACKXW0rt+fzLbjjRV52P2vFTaHhM5N9HhxYASiQ5WuZcbMycLh0zbkGJ0IMyjQvpEO4/uFo0ZYharTVZEkCbM2WvD1bhsKLBJa11ZhYl89kqKU5a63eJcVc7dbkG2U0LiGEi/20SMl0Z1eq13CtB/N+P6gHXanhI711ZjYV4/oYHnYzGV7rXh+hdnrtrc9HYqo4AoNr3nFJEnCrJ+M+HqXubieazBxQCiSYi5Tz7eZMHezCdmFIhrXVOPF/iFIqa1x/f6+D3Pw6792j3WGtNNj8kD5Vf9lvxbh+SUFXre9bWIMooLL3+/XavGvNszdbisuNwVevEWHlISy/+aaP+yYudGGM3ki6kQp8FRPLbo2dO+jn/6048vddhzKcCLfDCx/yIAmcZ7b+2q3Dd8ddOCPDCdMNuCXZ4IRqgvsSXLxrzbM3WF374fe2svsBwdmbrLiTJ6EOpEKPNVT49oPdqeEmRtt2HzUidN5IoK1AjrUVWJcTw1qhPi3Hl8tbfsR0HZ5GIqQGDgz/kDRty/AeXqv12U1N90LTetBUNZoDABwnjkA85opHstHTDvndd2i71+BdcsHvs/AFVr8m92zfG/WXL58N9uKy1eQy7dBifLdZMfmow6czpPc5dtD7Srf03kiPtxqx87jTmSbJMQGC7ithQqjO6mhUQaurgd1TUdwr0ehDI2F/fRB5H31LOzH93hdVpfaFyG3jIMqph6gVMGR+S+M696HeddXHsup4hoh9I5J0DbqCCiUcGT8hQsf3Q9nbuDGLw7UeeyiZXutmL/DiuM58vHfu6kaL/etnGuXimh4Qyf0Sh+H2s1aIbxGPD54eBD2r1t5+RUDwNfnLEmSMGuTDV/vtct1pZYSE/vokBTlWaabjjjwwRYr/soUoVUBN9ZR4f0hegBAbpGEp5eb8dd5EXlmCVFBAnokqzCuhxbB2so73iVJwqzv8/D1dqN8HVNPi4lDosq9bv31qAVz1+Xj0EkbsgqceO/BGKS1DPJY5qd9Jnz5cyEOnbQhv0jE8gk10SRRW8YW/W/xb3bM3eVw14FeaqTEl1MH/nRg5mY7zuQXt+fdNejawL38rC02/PCHE+cKJaiVQLM4BZ7oqkbLEvVqzNdWHD4vIsckIUwHtK+rxPju6ko9p1f2eQwAxnxlcedbL6B9kgLjK/laRpIkzFp38T5UQus6F+9DL9Oe7zBj7hazfB8ap8KL/YI87kNfXm7EjqM2ZBaIMGgFtKqtxlO9DagXK++jXJOIp78qxF/nnMgrEhEVrECPJhqMu9mAYJ3/8y9JEmatN8v5thTnu1/Q5fO904K5W0vku6/Ble+8IhGz1pux7agNGXkiIoMU6NlUg8fT9AjxkqfcIhH9Z+XjfIGIX16MQKj++rqGJbqeVfhoWb9+PZ5//nmkp6fjgQce8PhUpk82m7FouxmT+gdjydgI6NUC0uflw2qXylznh/0WTP3OiLFpQVj2aASSa6qQPjcfOUax1LILfjZD8DJ99qEzdkQFKfDGkBB892QkRvcwYMaPJny23XugyNc+WV+IRVsKMWlwJJY8WQN6jQLpszPLz/ceE6Yuz8XYm8Ow7OmaSI5XI/3DTOQUypO8FFlFjPwgC4IAzH8kFp8/UQN2p4Qxc7Igiu7ttm2gxdsjorH6hXjMfCAGJ7MdeHxett/zDACf/GzFol1WTLrNgCUPhkCvBtIXmcrP90Ebpv5oxthuOiwbFYLkOCXSF5k8ynvKGjM2HrFj5mADFo4IRmahiEe/NLl+36e5BlufCvX4dGqgwo1JSr8HAAHgk00mLPq5CJMGhGLJo1HQawSkf5Jbfr73mTF1VSHG/l8wlj0RjeR4FdI/yUWO0XNSn0Ft9dj6Uozr8/StIa7f9Un1/N3Wl2LQqZEGN9ZT+z0A+MMhO6b+ZMXYrlose8ggl9viIuSYSh+nALDnlBPjl1pwZys1lj9kQFqyCo98ZcaRTHd+zXagTS0lnupZ9g2CxQ50rq/EqE6aMpepTD8csmPqWhvGdtFg2YMGJNdQIP1zc/n7YZkFd6aqsfxBA9KSlXhkicW1Hyx24I9zIh7urMbSdANmDdLhWI6Ih7+yVGa2Lkudcjv0fSfBsv4tFLzbC86MQwge+QWEIO890FX1OsC+bwWMHw9E4Qd9IeafRXD6lxBC41zL5L3awuNj+voJSKII+8HvKitbpfxwyCGXb2c1lqXr5fL9woIck/dje88pJ8Yvt+LOVBWWP6iX6/kSK45kyvVBLl8nHu6swdJ0PWbdqZXLd4nVtY1jOSJECZjcR4PvRunxXC8Nvtpjx9sbbZWSZ2/0be5A2J2vofC7acj8XzfYTx9E9KNLoQjxXt5iUS4KV7+FrDd6IfPVTijasRgRw96DtmkP1zLK6CTEPLUajvN/I3tGX2S+2gmFP0yH5AhsXQ/UeQwAPt1uwTvrLXiwkw7fjQ3Bp8OC0alB+Q8PA01jCMLpwwfwxSuPBzop5fLHOeuT7TYs+sWGSbdqsWSkQb7GXVwEq8NdV378045nV5gxIFWNFaOC8PkIA/o2dwcSFQLQM1mFD+7SY83YIEzpp8OOf52Y+H3lHgefrCvAos0FmHRXFJY8VVO+jnn/PKx27/sHAMxWEY0TNHh5SGTZy9gktKmvw1P9I/yR7Kvywx8OTF1vx9hOaix7QIfkWAHpX1rLbs9POzF+hU1uz0fqkNZIiUe+cbfnAJAUpcBLN2uwMl2HxffpkBAmYOSXVlwosc22dRR4+w4NVo/WYeZALU7mSnh8WeW154E4jwFA2yQl3h6gxeoxejnfeRIeX2r19if95pMtZizabpHvQx8Ol+v15e5DD1gx9XsTxvY0YNkj4UiuqUT6vAKP9rxZggr/uzME34+LwCcjwiABGDmvAM7i+zGFAujZVIMPhoVgzfgITLkzGDv+sWPiClMZf9W3PtlqwaIdFky6PRhLxoTJ57H5BZfP9w8mjO2hx7KxYXIbOb/Qle/MQhGZhSKe6R2EVY+FY8rAYGw9YsMLy7zn6cVlRiTH+fdehKiqqlD0YvLkyejVqxfWr1+P7Oxs5ObmenwqiyRJWLjNjNE9DOjZTIvkmipMGxKCzAIR6/4o+yQw/2czBt2kw8AbdGhQQ4XJ/YOh0whY+pvnBdGfZx34dKsZr98ZUmobA2/U44V+wbipnga1opTo10qHAW10WHvQ/ycfSZKwcHMBRvcKQ88WBiQnaDBtaBQy851Y93tRmevN31SIQR2CMbBdMBrEqTF5cCR0GgWW7jQCAPYcs+LMBQem3BuF5HgNkuM1mHpvFA6esmHn3+58De8eitQkLRIiVWhdV4uH0kKx/4QNdmfZDb/P8r3TitFddOjZWI3kOCWmDQhCZqGIdYftZa43f7sVg9poMLCVFg1ilZjcVw+dGli6V75AKrRIWLrXhmdv1qNdPTWax6swpb8Be085se+UAwCgUwuICVG4PkoFsOuYA3e29v/TZkmSsHBrEUb3DEbP5jokx6sx7a4wZBY4se5Q2Rfx87cUYVBbAwbeaJDr+YBQ6NQClv7iGajWqwXEhCpdn5JPEHWX/E6pELDrHxvuvMn/vUbm77BhUGs1Bqaq0SBGicm3auX07/Ve1ot22dCpgRIjO2hQP0aJx7tr0bSmAot/dS9/e4oaY7tq0b5e2T0o72+nwUOdtGiZeH1cWMzfacegVhf3g8K9H/Y5vC6/6Bd7if2gKLUfQnQC5g3V45ZmatSLViA1UYmXbtHiUIaIs/ll34xVNl3nUbD+shi2376EmHkERcufAexmaG68y+vyRV+OhXXnfDgzDkHMOoqib8ZBEBRQN+jsWkYyZnl81E1vhuPfbRAvnKysbJUyf5cdg1qp3OXbR1NcvmXU81/t6FRfiZHtNagfrcDj3TRy+f5Wonzv1eOWpirUiyou394aj/LtXF+FKf206FRfhVoRCvRopMID7dRYe9jp9W9WhuC0h2HathBFOz6HI+Mv5H0+DpK9CIYOQ70ubzuyDZZ938Nx7gic2cdh2vAR7GcOQVO/nWuZ0NtfguXgWhQsmwj7qd/hzD4Oy4HVEAsr56GVN4E8j+WbRczcYMG0AQbclqJB7UglkuOU6NH4+g4CHtryI759ZxL2rb0+e/9d5OtzliRJWLjLjtGdteiZrEZyDSWm9dchs1DCusNymTpECf9bY8XT/6fFXTdoUDdKgQYxStzSzF2mYXoBd9+gQYt4JRLCFWhfT4W7b1Bj98nKO94lScLCjQUYfXM4eqYUX7cOi0FmvgPr9pd93dqlmQFP3BaB/7uk919Jt98UjLG3hKN9ss4fSb8q839xYFCqCgNbquT2/BYNdCoBS/eXcb7+1YFO9RUY2U4tt+ddNWgap8Di3e7lb2umQoe6StSKUKBhjAIT0jQwWoG/SgQKh9+kRmqCEglhCrROVOKh9irsPyP6/br8okCcxwBgeFs1UhPlet26lhIPdVBj/+nKy7frPrS7Hj2bFt+HDpYfwKz7o+wg7PytZgy6sfz70CE36XBjXTUSI5RolqDCE/9nQEa+iDO5cv7D9Arc3U6PFolqJEQo0b6BBne31WH38bLPIz7Pdzc9ejbVIDlOhWmDivP9Zzn53mbBoBu0GNhGhwaxKky+PUg+j+2W7zEb1VBh1j0h6NFEg9pRSrSrr8aT/2fAxsM2OC4p0y92yT0QH+ik92teiaqqCgUBZ8+ejfnz52PXrl1YsWIFli9f7vGpLKcviMgqFNGhgbvHTohOgZRaauw74f2Ea3NIOHTG4bGOQiGgfQM19p1wN5xmm4SnvizAy7cHI+YKu5UXWiSEGfz/asXpHCeyCkR0aOS+4AnRK5BSR4t9x7wHIW0OCYdO2TzWUSgEtG+kw77jNtcyggBoVO48aNUCFAKw+1/vwaY8kxOrdhehVZIWaj+/RnY6V0SWUUKHEgGcEJ2AlASl6ybnUjaHhEMZTo91FAoB7eupXOscOuuA3QmPZerFKBEfJpS53RX7bNCpgZub+v/m6fQFp1zPG5ao53oFUmp71tmS5Hpu91hHoRDQvqGm1Dqr9prRbuJ53DY9G2/9UAizreyLpxW7zdCpBdyc4t+LbZtTwqEMER3qugNxCkFA+7pK7DvtPVC177QTHep6Bvc61ldh3+nABTauVfn7wXu+5P3gGcDsWK/s/QbIbZcABPy1ZxelGsqEFDj+3uL+TpJgP7oVqto3XNk21HpAqYJUlOf110JwNNSN02D99fNrT28FlVm+SUrsO1NWPRcrUL64bPkWWuRgQUAo1VDXToX1z03u7yQJ1j83Q1PvxivahDa5C1Q1GsB2dLv8hSBA1+L/4Mg8iqhHv0HcG0cQ8+xa6Fr28X36r0Igz2Pb/3FAlIDzBRL6zCpA17fy8cQSEzKuo+D/f5U/zlmn86TiuuLepquuFC/zR4aI84USBEHAHR+b0HmGEQ8uLvLoTXip84Ui1h524MY6lfeg63SOA1kFTnRofMl1a5IW+45Xbs8tf3HVgST3PYNcBxRlt+dnRHRIurQ9V2DfGe/lZ3NK+GqvAyFaoHEN7/cmeWYJqw450SpR4ffr8otpuh7OY3lmCasOOiot30Bxe14oebkPVWHfyXKuz8860KFED2yFQkD7+mrsO+n9HFBkk7BstwWJEQrEhXkv9/MFTqw9ZMWNdSvhvuTieay++2+F6BRISVSVmQd3vi+9/9aUua8A+fo0WCtAVaJMj2Y68MGGIky7MxheXtYjoitQoUHcbDYbOnToUOE/arVaYbV6nvQ1dhFa9dXFJLOKuw9HBXu2ANHBCmR7ebUXkMcPcIoo9QpndLACx7LcjdCU74xoVVuNns2urKfXnhN2rD5gxezhYVeThQrJKn59NyrE8wQaHaJEdmEZ+TY55XyXWkeBY5lyvlOTtNBrBExfmYcn+4ZBkoC3VuXBKQJZBZ7bnb4yF4u3GmG2SWiZpMHsh2J8lb0yZRnl4JS3sss2eg9c5RZJZZd3tsO1XbUSpcaSiCpnu0v32tC3hfyk09+yiss0KuTSPJRX3mXVcyWOZbqf0vVtpUd8hBKxoQocyXBg+g+FOJ7lwKz7vb9Ws/SXIvRtpfN7vnOLJDglICrokvQHCTiW7f3iONsolW4LgoQyy/C/wLUfvOTrWLb3ss82ymM+eSwfrEC2yfuFmdUhYfp6G25trqrU8aHKIxgiIShVEI1ZHt9LhVlQxjS4om3o+7wEseA87Ee3eP29ps0QSFYj7Ad/uOb0VpS7nl9aXgKO5VxF+QYJyC7jlUOrQ8L0DTbc2kxZZvmeuCDis9/seKZnYF6BVwRHyeVd4FnezsIsaOMalrmeoAtF3NRDENRaQHQi74unXIFERUgMFLoQBN/8BApWvo6C5ZOgbZaGyFGLkP32bbD9vd2fWSpTIM9jp3JFSBLw0VYLnr9FjxCtgJkbLHhgoRHfjgnxeABIV8cf5yxXXfHSPpQsUwB4f7MVz/bSIiFMgU932jBsgRlrHglCeInA/rilZmz4ywGLA+jeSInXbqu8nnNZBeVctxb8dx/UlVRmex5Ugfb8krZg499OjF9hhdkOxAQLmHe3FhGXdDqYvsGGxbsdMNuBlgkKzB5UOeMiBvo8Nn29DYt/s7vzPaQS6/XF63Nv7XlZ1+dl3YeGeN6HAsDnO8yYvsaEIhtQN0aJeSPDSrXT474owIY/bbDYge5NNHhtQPC1Zuuyys13mfffF89jXupJVhnnPpOIDzeZMfhGd5naHBLGf2XE07cEIT5ciVMX+BDrmjCKWm1VqCdgeno6Pv+84r0npkyZgrCwMI/PlKWXH6R71V4LWr+c5fpc2jXYVzb8YcWuf+x47rYra0iPnHNg7MJ8jO1pQKdGvr+JWvWbCa2fPuX6+CvfkcFKvDMiGhsPmtH6mdO4ccJpFJpFNE1Ul5o8aGSPUCx7Og5zx8RAKQiY8FkOJMm36Vp1wIbWr+e5Po7rpJ3fe8qBf7JEDGztnxvmVXvMaP3CedfH4cfr4yHtDOicrEVyTTVua63HtLvCsPagFSezSweM9h634Z9MJwZWwqvAVDnsTglPfCP38p3UJ3ADqfuattsj0LS8HaaFIwCH914m2hvugm3vsjJ/XxXYnRKeKB4fqazyPV8g4sEvLOjdRIXBra/v10IvJVkLkfl6F2RN6YGCb19D2J2vQ9Ooo/xLQb68sexfDdP6D2E/fRDGH9+B5fcfEdSl8sYuvp7OY6IE2J3AC7fo0bmBGqm1VHjrTgNO5IjYddz7QwK6vl0crnlUJw1ubqJG83glpvTTQRDkSUdKeu5meazCD4bocSpXwtSf/Nf2rfrViNbjTrg+/ryOqQ7a1lFg+Ugdvrhfi871FXhiua3UeHsj28njEM69WwulAExYZfX5dXkgXO48NrK9PA7h3Ht0cr5X+i/fq/Za0Hpituvj73p9Wystlj0agUUPhSEpWoknPi895t5zfYOx7JFwfHBfCE7lODH1e9+PCbhqnxWtJ+e4PpVxHjNaRIxaWIj6MUo80tP9yu9bPxWhfowS/VKrzjUrUSBUqCegxWLBxx9/jHXr1iElJQVqteeNw4wZM8pd/7nnnsO4ceM8vtOs6X3Zv9u9qQYptdyDA9uKg2E5Rgmxoe7lso0imtT0nrUIgzye26WTgGQbRdcMejv/sePkBSdumuw5btBjnxWgTZIai0aFu747et6BEZ/kYfBNeozpWfaYJdeie3M9Uuq4B7e3FQ8InVPoRGyY+6lqdqETTRK838RFBCnlfBd6nrGyC0VEl3gy26mxHmtf1iPX6IRSISDUoECnF0+jVpTn/owIViIiWIm6sWrUj1Oj28Sz2HfchlZ1fdcod09WIyXBPR6jrTjpOUYRsSV6xWUbxVIzvLrSaRDKKW85shkTLMDuBArMokcvipwSy5T0zW4bmsQp0TzeP7Mhd2+qRUrtKNfP7vIWEVtiluZsoxNN4ssq77LquRPR5bzenlJb3t6JHCdqXzLD9je/mNEkXoXmif4PFEQYBCgFFA+oXiLPJqnUTJcXRQcLyLnk6bm8/H/3KZdrP1xFvqKDhVI3B9lGEdGXPHW3OyU8udSCs/kS5t+nv256AQKAVHQBktMBRXAMSrZYQkgMxMLMctfVdhkDXbdHYZwzGM5zf3pdRpXUFsrYhjB9PsqHqb567np+aXldZfmaJERf0gPJ7pTw5DKrXL5DdV7L93yhiGGfWdAqUYlXbg3cRDiiMUcu71DPHuXKkBg4C8opb0mCM+sYnADspw9CFdcIITc/iZwj24q3aYcj47DHKo5zR6Bp0M779vzgejqPxYTI/zaIcf+dyCAFIgwCMvKuk6ds/1H+OGfFFP+bY5IQW2Jo6myjhCZxCo9lGsS4/4ZGJaBWuICMfM9txwQrEBMM1IsGwvTAvfPNGNNZ41EPfaV7CwNSktzXg57Xre5ri+xCJ5okXh+TcF2rMttzk1Tq/HtRme35Je2/QSOgTqSAOgBSE5S4+UMzvtnvwKgO7uuxCIOACIOAulFA/SgB3d6zYN8ZEa38PL5xoM9jHvmO1qLbu2a/5Vu+D3W/KeO+DxURG3pJe36196GFYqnr8xCdAiE6IClaiZa1VGj7Sg7WHrKhb4kA2MWxyuvFqhBmUODej/IxpofBIz3XqnsTDVJqhbt+dh3PV5Xvi+exy9cTo1VC+oJCBGkFvHdviMfr3bv+sePIeSd+fCkHAHAx3tv+f7kY1VWPx9LYUYHoSlSohThw4ABSU1OhUChw8OBB7N271/XZt2/fZdfXarUIDQ31+FzJq8DBWgXqRCtdnwaxSsSEKLDjqPv1RqNFxIFTdqTW8d4IaVQCmiWoPNYRRQk7j9qRWkc+mT7YzYBvH4/A8sfcHwCY0DcIUwa5r8T+Pu/A/XPy0L+1Dk/e7J8AIAAE6xSoE6N2fRrEqRETqsCOI+5x+owWEQdOWJFaRhBOoxLQrJbGYx1RlLDziAWpSaUvwCKClQg1KLDziAU5RhHdm5c98KpYfB6zOXz75C1YK6BOlNL1aRCjQEywgB3/unssGC0SDpxxIrVWOeVdU+mxjihK2HnM4VqnWbwKaiWw45h7mX+znTibL5XarskqYfUhm996AQLF5R2tcn0a1FB5r+cn3XX2UnI9V3up57Yy1wGAw2fkfXDpTYHJKmL1AQsG3lQ5A/BqlAKa1VRgxzF3CEiUJOw85kRqove2IjVR6VGGALD9XwdSr5MJPirCtR+Oe9sP3vMl7wfPYP/2S/bbxQDgiQsSPh2qL/VaUcA57XCeOQBViUk9IAhQN+gEx8nfylxN23Us9D2fhHHe3XCe2V/mcpob74Hj9H44M/7wZaqvWpn1/LgTqQll1XPP+gCUUb7LrDhxQcSn9+q8lu/5AhHDFlnQLE6B/92mgSKQr4Q47bCf3Adt467u7wQB2sZdYPv31yvfjqCQXw0u3qbt+F6oani+TqyqUR/OnFM+SPSVuZ7OY62L/z2W464/eUUicoskxIf7f5b7qswf56zEcEGuKyW2abQW15XiZZrHK6FRwuO1S7tTwpl8CfFhZR/TF3sQ2vzUk8n7dasSO/4qcd1qFnHguBWpSVWjR4/7fO0uC7k9F8tuzxO8teciUhPKv24RpfKvuf1dviUF8jx2KVe+/dSx2ft9qIAd/1x6H+pAau1yrs/jVdjxj7unrihK2PmPHam1y+9cIMEdePTGXe5+vh+LVRafx9x5MFpEHDjtKDMP5efbva+MFhEjPy2AWgl8MDQE2kuGH3r3nhCseDQMyx+RP6/eId9/f/ZgKO5tF/jJgYj+KyrUlWnjxo2+TkeFCIKAYR31mL2hCEnRSiREKvHuTybEhiqQ1tR9UTF8Th7SmmkxtIMcwBjeSY8JXxeieaIaKbVUWPCzGWabhAFt5Mbj4lOVS8WHK5EYKZ+Yj5xzYPicPHRqpMHwzgbX+AhKAYgs46mvT/PdNRSzf8pHUowKCVEqvPtDPmLDlEhr4X4CMvy980hLMWBoFzlwObxbCCYszkHz2hqk1NZiweZCmG0iBrR1v/a8dKcR9ePUiAxWYN8xG15flov7u4agXg25gd5/3IrfT9rQpp4WoQYFTmU7MPOHPNSOVvm0F2CZ+W6nxewtViRFKZEQocC7G8yIDVEgrcTshsPnG5HWRI2hbeX0DO+gxYTlRWieoEJKghILdlhhtgEDWsmBvBCdgIGtNJi2xowwvYBgrYDXfjAjtZay1E3Z6oM2OEWgX0rlvTYnCAKGdTZg9nqju57/aERsqBJpzdwnvOEfXUBacy2GdpRPiMO7GDDhq/zieq7Ggq0muZ7fKB8HJ7Md+G6vBV2aaBFuEHAkw4EpKwtxQz01ki/pYbh6vwVOp4R+rStvFq7h7TWYsMKC5vFKpMQrsGCXHWa7hAGpctqeXSGX/fiecjnf11aDYQuKMG+HDd0aKvH9QQcOnRXxSl/3PsozS8jIF5FZKF8gXbxxig4WEFN83GYZRWQbJZwsHmfkyHkngrQCaoYpPMZXqizD26kx4VsrmtdUICVeiQW/2OT90FKum8+usCA2RHDvh5vUGLbQXLwfVPj+kF3eD7fKv7c7JTz+jQV/nBMxe4gOTklyjT0VphegqaQBtS/HsvUjBA2eCefp/XCc3gtdpwcBtQG2374EABgGz4JYkAHLmv8BALRdH4G+19MwffEwxAunIATLvcokmwmwlZh9UhsMTcptKPpuUmVnyavhbdWYsLK4fBOU7nresrief2uVy7eH3F7dd6MawxZZMG+nHd0aKPH9oeJ63qdE+S614o8MEbPv0not34sBwPgwAc+maXChSIJ8ewHXcVDZjOs+QMTwD2A/sRe243sQ3GMMBE0QirYvBgBEDP8QzrwMFKx4BQAQfPOTsJ/cC0fWMQgqLXTN/w+GdkOQ9/l49zbXvovI9HmwHt0O619boWuWBl2L3siecVtA8ggE9jxWN1qJno1V+N9qMybfJi8zY50Z9aIVaFvXPz3bfUFrCEJMnfqun6MTk5DYJAWmvFzkZlReQPdyfH3OEgQBw9qqMXurFUmRCiSEC3h3kw2xIQLSGsvlFawVcNcNaszaZENcqALxYQrM2yEHJHoXT1y2+W8Hsk0SWsQrYNAIOJop4s11VrSupURiJQV/BUHAsO6hmL0mH0kxavm69ftcxIapkNayxHXru+eQ1tKAoV3l13tMVhEnS4yTdjrHgT9PWxFmUCI+Ut4HeSYnMnIdyMyXg0rHzssRoOhQJWJCK7deD79JhQmrbMXnawUW/OKQ60BK8fl6ZXF73v1ie67CsM+smLfLjm71lfj+DwcOZYh45Rb590U2CbO329GjoRx0yS0CPt9tx/lCCb2byNvcf8aJ3zNEtKmlRKgOOJUrYeYWO2pHCGhVRhDO5/kOwHls/xknfj8rok0tBUJ1gpzvzTY532UE3n3NfR9qltvzSCXeXVskt+dN3Z0Fhn+Sj7SmGvd9aOfi+9AElXwfus3icR966oITPxywomNDDSKDBJzLFzFnsxlalYCuyfJ2Nx+2IdsookWiCgatgKPnnXhztQmt66iQGOHfh9+ufG80u89j64rz3aREvucW57t9cb476jBhqRHNE5RISVRhwfaL+ZbL3WgRMXK+PEHhm4NCYLRKMFrlco8MEqBUCKgd5Zm33OIepfVjlKXGxCWisl3z2fH0aXksv8TExGtOTEWkd9XDbJPw8rJCFFgktElSY86IMI8nBydznMgtMdhsn5Y6XDBJmLXWhKxCEU3iVZjzQFi5r0le6sffrbhgkrByrxUr97rHVYkPV2DDhKhy1vSN9J4hMNtEvPzVBRSYRbSpp8Wc0bGX5NuBXJP7SVuf1kG4YBQx64d8ZBXIr2DMGR2L6BKvmB7PdODt7/KQXyQiPlKF0b1CMbybu/ejTiNg7YEizFqdD7NNREyoEp2b6DGmV2ilDCqe3kkLs13Cy6uK5PKurcKcoUGe+c51IrfInac+zTVyeW8wI8sooUmcEnPuC/J4Ree53nooBODxr4pgc0jo1ECNl28tHfD6Zq8N/9dEXeknmvRuQXI9/6YABRYRbZI0mJMe4aW83TeRfVL1uGASMevHwuJ6rsac9AjX699qlYDtR61Y8LMcHKwZrkSvFjqMSSvdq/WbX8z4vxa6Ss13n2Zqudw2WeVyq6HAnHsMrnI7my9BENzHdetaSkwfoMM7G614e4OEpEgF3huiR6NYd13Y8JcDz69090QYt1T+/9guGjzaTb4I+fI3O97f4n6qO3SBGQDwv346181cZerTTI0LRRJmbbaV2A96934oECEI7nJpXUuJ6XcU74eNNnk/DNa59sP5QgkbjsjtQv85Zo+/tV62p0MAAQAASURBVOA+HdomXR/BAPuBb2EOioKu1zNQhMTAefYQjPPuhmSUh2lQhCcAkrv8te3uh6DSIvi+uR7bMa+dDsu66a6fNS37AwBs+ytvJvvy9GmmKi5fO7JMNrl879a5Xo85m++lfPtr8c4mW3H5CnhvsBaNYuVlPMvXc1b3BUN1aJukxLZjTpzIlXAiV0LXdz3rwOEX/dervTzm3cuhCIlGyG3PQxkaC/vp35E9606IhfJkIcrIREglylvQGhB+93Qow+Mh2S2wn/sbufNGwbzbXa6Wfd8j7/NxCOn9JMIHT4X9/FFc+HgYbP/srPT8lRTI89i0O4IwZY0ZoxebIAjATUkqzLkvuNJm06yIOs3bYPxna10/D37+TQDA9mULsWDCg4FKVin+OGeld9DAbANe/s5SXFeUmHOvAdoS11tPp8ljwD27wgKLXULLBCXm36d3zfatVQFf77Fh6o8ibE4gLlRAr8ZqPNipcl/DTU8Lhdkq4uUvsuXr1vo6zHm4hsebQCez7cg1uq9bD56w4v53z7t+nrosFwDQv20Qpt4nP+jZ8HsRnv8sx7XMuE/lNmPsLWF49Fbvk5z5S5+mxe35FjuyTMV1YIjW3Z4XSB7j8LdOVGL67Rq8s9mOtzfZkRQh4L073e25UgEcy5bw2AEbcs0SwvUCWtRUYPF9WjQsfgVcpxaw9i8nZm21w2yTXxHvXE+JMXeoKm2yn0Ccx3RqAWsPOzBri+jOd30lxnRSV+okR+ldiu9DlxvlY7ROGfehRSXuQ1O08v3YuiL5+rymCnNGhLruQzUqYPcxOxZuM6PALCEqWIEbktT4YkyYazIOrRr4+lcLpn7vhM0hIS5MgV7NtXiwa+U8rE/vrJPzvaJEvoeHeub7gtzT3CPfJhGz1pvd+R4e4mojD511Yn/xbPa9ZuR5/L11T4X7PbhJVJ0IUgVGTxVFEa+99hreeustGI1GAEBISAjGjx+PF154AQrF1QcKpOWdrnqdKkFfeT2rrit5BYFOQWAYqmlX9ULfD1T8n1AFBuWuiLzfzwQ6CQER3jw+0EkIiLPbjgU6CQER361RoJMQEKNf3hfoJATE7IktAp2EwIj1/4Pt69LZ8sefrbIunQmwugiqpvdjYvW8ThXu3B7oJFwXpJ/7BzoJASd0WhHoJAREhbp8vPDCC5g7dy6mTp2Kjh3lmfh+/vlnTJo0CRaLBa+//rpPE0lEREREREREREQVV6Eg4IIFC/DJJ5+gX79+ru9SUlKQkJCAhx9+mEFAIiIiIiIiIiKi60iFBvi6cOECGjduXOr7xo0b48KFC9ecKCIiIiIiIiIiIvKdCgUBW7Zsiffee6/U9++99x5atmx5zYkiIiIiIiIiIiIi36nQ68BvvPEGbr31Vqxbtw7t27cHAOzYsQMnT57E6tWrfZpAIiIiIiIiIiLykeo6ERBVrCdg165d8ddff2HAgAHIy8tDXl4eBgwYgCNHjqBz586+TiMRERERERERERFdgwr1BASAqKgo9OvXD+3atYMoigCA3377DQA8JgwhIiIiIiIiIiKiwKpQEHDNmjUYNmwYcnJyIEmSx+8EQYDT6fRJ4oiIiIiIiIiIiOjaVeh14EcffRSDBg3C2bNnIYqix4cBQCIiIiIiIiIioutLhXoCnj9/HuPGjUONGjV8nR4iIiIiIiIiIvIXoUL9wagKqFDJ33nnndi0aZOPk0JERERERERERET+UKGegO+99x4GDRqErVu3okWLFlCr1R6/f+yxx3ySOCIiIiIiIiIiIrp2FQoCfvHFF/jpp5+g0+mwadMmCILg+p0gCAwCEhERERERERERXUcqFAR84YUXMHnyZEyYMAEKBd8lJyIiIiIiIiIiup5VKAhos9kwZMgQBgCJiIiIiIiIiP5LODFItVWhkr///vvx1Vdf+TotRERERERERERE5AcV6gnodDrxxhtv4Mcff0RKSkqpiUFmzJjhk8QRERERERERERHRtatQEPD3339Hq1atAAAHDx70+F3JSUKIiIiIiIiIiIgo8CoUBNy4caOv00FERERERERERER+UqEgIBERERERERER/QcJykCngAKEU8IQERERERERERFVcQwCEhERERERERERVXEMAhIREREREREREVVxDAISERERERERERFVcZwYhIiIiIiIiIio2mB/sOqKJU9ERERERERERFTFMQhIRERERERERERUxTEISEREREREREREVMUxCEhERERERERERFTFcWIQIiIiIiIiIqLqQmB/sOqKJU9ERERERERERFTFMQhIRERERERERERUxTEISEREREREREREVMUJkiRJgU4EAEhftg10EgJDEgOdgsAQr4tqV/kUQqBTEBhKZaBTEBhWW6BTEBiqalre1bWeC9W0Xauu+a6m7droyb8HOgkBMXtm50AnITDy8gOdAqpM1fS+5MSGI4FOQkAkzS0IdBKuC9IvQwOdhIATbvos0EkICE4MQkRERERERERUXXBikGqLJU9ERERERERERFTFMQhIRERERERERERUxTEISEREREREREREVMUxCEhERERERERERFTFcWIQIiIiIiIiIqLqghODVFsseSIiIiIiIiIioiqOQUAiIiIiIiIiIqIqjkFAIiIiIiIiIiKiKo5BQCIiIiIiIiIioiqOQUAiIiIiIiIiIqIqrsKzA//999/YuHEjMjMzIYqix+9efvnla04YERERERERERH5GGcHrrYqFAScM2cOxowZg+joaMTFxUEQBNfvBEFgEJCIiIiIiIiIiOg6UqEg4GuvvYbXX38dzz77rK/TQ0RERERERERERD5WoT6gubm5GDRokK/TQkRERERERERERH5QoSDgoEGD8NNPP/k6LUREREREREREROQHFXoduEGDBnjppZewc+dOtGjRAmq12uP3jz32mE8SR0REREREREREPsSJQaqtCgUBP/74YwQHB2Pz5s3YvHmzx+8EQWAQkIiIiIiIiIiI6DpSoSDgsWPHfJ0OIiIiIiIiIiIi8pNr7gMqSRIkSfJFWoiIiIiIiIiIiMgPKhwEXLhwIVq0aAG9Xg+9Xo+UlBQsWrTIl2kjIiIiIiIiIiIiH6jQ68AzZszASy+9hEceeQQdO3YEAPz8888YPXo0srOz8eSTT/o0kURERERERERE5AOcGKTaqlAQcNasWfjwww8xbNgw13f9+vVDs2bNMGnSJAYBiYiIiIiIiIiIriMVCv9mZGSgQ4cOpb7v0KEDMjIyrjlRRERERERERERE5DsVCgI2aNAAS5YsKfX9V199hYYNG15zooiIiIiIiIiIiMh3KvQ68OTJkzFkyBBs2bLFNSbgtm3bsH79eq/BQSIiIiIiIiIiIgqcCgUBBw4ciF27duHtt9/GihUrAABNmjTBL7/8glatWvkyfURERERERERE5CucGKTaqlAQEADatGmDzz77zJdpISIiIiIiIiIiIj+44vBvQUGBx//L+xAREREREREREVUnFy5cwL333ovQ0FCEh4dj5MiRMBqN5S7/6KOPIjk5GXq9HrVr18Zjjz2G/Px8j+UEQSj1+fLLL686fVfcEzAiIgIZGRmIjY1FeHg4BEEotYwkSRAEAU6n86oTQkRERERERERE9F917733IiMjA2vXroXdbseIESPw0EMP4fPPP/e6/NmzZ3H27FlMnz4dTZs2xYkTJzB69GicPXsW33zzjceyn376KXr37u36OTw8/KrTd8VBwA0bNiAyMhIAsHHjxqv+Q0RERERERERERFXRn3/+iTVr1uDXX3/FDTfcAACYNWsW+vTpg+nTpyM+Pr7UOs2bN8fSpUtdP9evXx+vv/46hg4dCofDAZXKHbYLDw9HXFzcNaXxioOAXbt2df2/bt26qFWrVqnegJIk4dSpU9eUICIiIiIiIiIi8hNODAKr1Qqr1erxnVarhVarrfA2d+zYgfDwcFcAEADS0tKgUCiwa9cu3HHHHVe0nfz8fISGhnoEAAFg7NixSE9PR7169TB69GiMGDHC61u65anQxCB169Z1vRpc0oULF1C3bl2/vg4sSRJmbbTg6902FFgktK6twsS+eiRFKctdb/EuK+ZutyDbKKFxDSVe7KNHSqI7+1a7hGk/mvH9QTvsTgkd66sxsa8e0cGlD47cIhH9PyzE+QIJv0wIRajevYzNIeH9TRasOmBDllFCTIiAsV11GNi64hUJABb/YsPcbVY5/XEKvHiLHimJZed5zSE7Zm6w4kyeiDpRCjyVpkXXRmrX7+X9aMXXe+zyfqylxMS+Oq/70eaQMHiOCYfPi1g+KghNasrLzNpowfubbaWW16uBvS+EXlN+y7L4VxvmbrcVl6MCL96iQ0pCOfvhDztmbrS590NPLbo2dJf7T3/a8eVuOw5lOJFvBpY/ZECTOPf28swSZm2yYtu/DmTkS4g0COjZWIXHu2kRoru6g+2K8xiAsp69xYpNRxw4fM4JtRL49bnS5bfjXwdmbrDiSKYTBrWA/qlqPNFDC5XSP/uhLJIkYdYGC77+zepuA/oZrqANsGDuz1ZkG0U0jlPixVsNHm3AV79a8d0BG/7IcMBkBX55Pszj2K5Mi3+zY+4Ou7ue36y5TD13YOZmG87kSagTKeCpnhp0bSDnze6UMHOTHZuPOnA6T0KwVkCHukqM66FGjRA5f6fzRHy41Y6dx53INkmIDRZwWwsVRndSQ+On8g1EPe/xdiHO5kse2x3XU4uHOsvts9UuYeJ3FhzKcOLfLBHdGqnw/t0GH+e8tEDV6dO5Tny4yYKd/zqQbRQRG6LAbS01GN1VB43K/8e1JEmYtd58Sb6DkBR9mXzvtGDuzxZ3vvsGufKdVyRi1gYzth21IyNPRGSQAj2bqPF4mh4hOnfeX/vOhD0nHfj7vBP1Y5RY8UiYX/N6Oa598atF3hd11Fe+L7aai/eFCi/2NSCllvu4eHmFETv+sSOzQIRBI6BVbRWe6h2EejHlb/da+fpcLUkSZm2y4eu9JY7vPjokRXm20ZuOOPDBFiv+yhShVQE31lHh/SF6AEBukYSnl5vx13kReWYJUUECeiSrMK6HFsHayj2PXYmGN3RCr/RxqN2sFcJrxOODhwdh/7qVgU7WFZMkCbNWF+DrnUYUmCW0rqvBxEERSIpRl7ve4q2FmLuhENmFTjSO1+DFgeFIqeO+hs4qcOLNlXnY/pcFJquEurEqjPq/UNzc0t1W95h8FmdzPe9FxvUNw0Npvr82vZrr6ZIudw602iVM+8mC7w86YHdI6NhAhYm36jzuS177wYI9pxz4O1NE/WgFVowJ9pq+edttWLLbjrP5IiIMAu65UYPRXSp+X3I9XqcePufExz9bseekE7lFEhLCFbjrBjWGtbu2+6/Lqez7EgD4arcN3x104I8MJ0w24JdnghHqp3uSKxXS/UGE9X4MyrAasJ06iJzPn4bt2G6vyxpa34awW8dDHVsPUKrhOP8P8n96D6Yd7nHNFKExiLzzFeia9YBCHwbrke3I+fxpODL/qawsUTUxZcoUTJ482eO7iRMnYtKkSRXe5rlz50rFyVQqFSIjI3Hu3Lkr2kZ2djZeffVVPPTQQx7fv/LKK+jRowcMBgN++uknPPzwwzAajXjssceuKo0VusO9OPbfpYxGI3Q6XUU2ecU++dmKRbusmHSbAUseDIFeDaQvMsFql8pc54eDNkz90Yyx3XRYNioEyXFKpC8yIccoupaZssaMjUfsmDnYgIUjgpFZKOLRL01et/fit0VIruG9gX9iiQk7jznw2u0GrH40BG/dGYS6l7mIv5wfDtox9UcLxnbTYtmoICTXUCL9M8/0l7TnpAPjvzHjztZqLB8dhLTGKjzypRlHzrsviD7ZZsOiXTZM6qvDkvQg6DUC0hcVed2Pb661IjakdFV5oIMWW8cHe3waxChwc9PyL/Aq6odDdkz9yYqxXbVY9pBBLsfFRcgxlbEfTjkxfqkFd7ZSY/lDBqQlq/DIV2YcyXTvB7MdaFNLiad6er9IyCwUkVko4Zk0HVaNDsKU23XYetSBF1ZZ/JPHAJW1zSmhdzMV7rpR4/XvHD7nxEOLi9C5gQrLRwVjxiA9NvzlwFvrrF6X96dPtlqxaKcVk/oZsGRUiJyfBcby24DfbZi62oyx3XVYNiZUrjsLjB771WKX0LmhGqO66CsjG2X64ZADU9faMLazGsvS9UiuoUD6FxbkmLznb88pJ8Yvt+LOVBWWP6iX6/kSK45kynmz2IE/zjnxcGcNlqbrMetOLY7liHh4ibvsjuWIECVgch8Nvhulx3O9NPhqjx1vbywd5PdJHgPYpj3W3bPdGtrWXeedEqBTA/e11aB9Pf8GSUoKVJ0+ll1c7rcb8N2joXjuFj2++tWKt9eZfZ5Hbz7ZapHzfXsQlowOLc534WXybcXU1UUY212PZQ+HITlOhfT5ha58ZxaKyCwQ8UxvA1Y9GoYpA4Kw9W87Xlhe+nw+sLUWfVp4b/Mq2ydbLVi0w4JJtwdjyZgw+dpmfkH5++KAFVN/MGFsDz2WjQ2T60CJfQEAzeJV+N+AYHz/RDg+GR4KCcDITwvgFMve7rXyx7n6k+02LPrFhkm3arFkpAF6tYD0xUWwOtz5+PFPO55dYcaAVDVWjArC5yMM6NvcfXOtEICeySp8cJcea8YGYUo/HXb868TE7/1zPr9WGkMQTh8+gC9eeTzQSamQT9YXYtGWQkwaFIklT8bKx/fsrPLr9J4iTF2Rh7G9Q7HsqTgkJ6iRPjsLOYXuuvDs4hwcy3Tgg/RorHwmDv+XoseT83Pwx2nP89Vjt4Ri6yvxrs/QzqUDZD7J51VcT7vyeQXnwCk/WrDxLwdmDtJj4YggZBZKePSr0m3zwFYa9GlW9nX366ut+GaPHc/20mL1I8H44G4DWpQTpLqc6/U69dBZJ6KCFHhjgB7fPRyM0Z01mLHOis92+ec6BgjMfQkgX9d1rq/EqE7Xx/nLcOMARA75H/JWTsXZyZ1hO/U7ajy5DIqQaK/Li6Zc5H83HRn/S8PZiR1QuG0xokd8AF2znq5lYh/5AqqYJGTOuhtnJ3eCI+ck4p76FoLG/w9mqXp57rnnkJ+f7/F57rnnvC47YcIErxNzlPwcPnz4mtNUUFCAW2+9FU2bNi0VjHzppZfQsWNHtGrVCs8++yyeeeYZvPnmm1f9N64qCDhu3DiMGzcOgiDgpZdecv08btw4PP744xgyZAhSU1OvOhFXSpIkLNxpxeguOvRsrEZynBLTBgQhs1DEusP2Mtebv92KQW00GNhKiwaxSkzuq4dODSzdK58YCi0Slu614dmb9WhXT43m8SpM6W/A3lNO7Dvl8NjWF7/IPRYe6FC6cd76tx2/nnDgo3uD0KG+GokRSrSqpULr2hXqcOlO/w4rBrVWY2ArTXH6ddCpBSzd6z3Pi3bZ0KmBCiM7alE/RonHe+jQtKYSi3+R8yvvRxtGd9G69+MdemQWSlh32DO/W/62Y9s/DjzTq3R+g7QCYkIUrk+OScLRLBF3tvZPEHD+Dpu8H1LVaBCjxORbtVewH5QY2UEj74fuWjStqcDiX93L356ixtiuWrSv572MGsUqMWuwHj2SVagdqUC7uio82UOLjUcccPjhJipQZf1Ydx2Gt9eiUaz3JuGHg3Yk11BgbDct6kQpcFOSCk/9nxaf/2qD0eq/m8lLSZKEhTssGN1Vh55NNEiOU2HawOI24M/y2gALBt2gxcDWxW3AbQa5Ddjjvjj8f/buOzyK4v8D+Hv3enoPpBAgoUOo0qSDiAgqICpSRAEBQRFQEUWKX38CFlCwoAhSBBUEFew0FWkiRRApigiElt6v7/7+2HCXI5cAlwsXyfv1PPdALrubmd3Zmd3Pzs481F6PRzvp0TT+xgV/3Fm2x4qBzdVF5VzErN5apQwcLKUM7LWiQ6IKI9ppkRghYkIXrVLOf1OWD9QLWDrYgDsaqlE7XESzOBVe6KXFkQsSzucoF6odE9WYfZcOHRLViA8V0a2uGo+01WDTsYrp1e3LOs1fC5d6y0/rfKDlpxUws48B97XUuu0FXhF8WaY71tFgdn9/dEjSID5MhW4NtHikgx6b/iz973qLLMtYsdOEMV2K5fvey/ku/aZt2Y6ifLcsyvddRfnepwS160arsfDBQHSrr0WNcBXaJmow8TY/bDtmhc3urKum9fHH4LZ6xIX6/lUYWZaxYocRY7oY0L1h0b4YGHAd+0KPpCg1Zt3t77IvAOD+1nrcUku5HmkUq8aTt/nhQo6Ec1nub1K9wdtttSzLWLHHijEddeheT4N60SrMvUfvcn7bJBkvf2fG07fp8EArLWqFi0iKVOGOYsGRYIOAQa20aBKjQmyIiHa11RjUSoN9ZyrnZHZHfv4eX74xEwc3/Xd6/10myzJW/JyHMT2D0L2JAfVitJg7OBypOXZsPlz6Q4ZlP+ZhYLsADGgTgKRqGswaGAq9VsS6Pc4g/sFTFgzpGIDkBB3iI9QY2zMYgQYRR866niv+OhGRQSrHx0/n/XP9etoel3xepQ3MM8lYt9+KKbfr0ba2Go1jVJh9t77Efcm03noMbq1FXKj7XmAn0+z45DcL3h5kQLf6GsSFimgco8KtiZ7fl1TW69QBLbR4/g49WtdUIz5MxF1NtejfXINNZbSj5eWL+xIAeKitFo920KFpGb0vb6TgnuOR9/Ny5O9YBeuF48hY+SRkixGBHYa6Xd50/BcUHvgK1gsnYEs7hbzN78KS8gf0ddoBANTRSdAntkbGyomw/Lsftkt/I+OjiRA0Bvi3ufdGZo2qAJ1Oh6CgIJdPaa8CT548GUePHi3zU7t2bVSrVg2pqaku69psNmRmZl51LL+8vDz06tULgYGB+Pzzz6HRlB1XadOmDVJSUkq80nw119UiHjhwAAcOHIAsyzh8+LDj5wMHDuDYsWNo2rQpli1bdl0JuB4pWRLS8mW0L1YxBuoFJMeqSgTrLrPYZBy5YHdZRxQFtKutdqxz5LwNVjtclqkdqUJMsOCy3b9T7XjnJxPm9vOHu9eutx63onGMGkt2mNHptRzcviAXc783wlTG08CrsdhkHDkvuU9/ivsL14Nn7Wh/RS+WW5NUjuVTsmT3+zFO5bLN9HwJL2wwYW4/A/Saq3czX7vfgprhIlollC/o6Y7FLuPIBQntaznzJQoC2tVS4WCK+5uZgyl2tK/lmpZbE0vfb9cqz6S8UqkWvdv13pfH+qppswO6K14P1KsFmG3K09cbxVEHJF6ZH3XZdcB5N3VAoqbUdXyl1HJeU4WD50or567LA8CttUs/LwAgzwQIQJmvj+SZlJtmb/N1OV/8iwVt5uah36J8LNlhdgkM+UJlK9N5JrlCjvuVnPl2XtwE6sVry3exda4l33kmSamzb/DQBdeqzH1xpqx9YUP7JGdPEFEU0C5Ji4Nn3N+AFlpkrN9nRlyoiGrBFRP8rIi2OiX78vnt3Kbj2q9omT8vSLiUp7yl0u/9AnScl49Rqwpdethc6VKehE3HbLgloXLcSN9MUjLsSMuV0L6u8+2gQIOI5AQdDv7r/kbFYpNxJMWC9nWdN1+iKKBdXdd1mtXS4psDhcgusEOSZHy9vxAWm4zWSa43bYu35KLNc+fQ79WLWLI1t0Lqek+usa6lDTxy3g6rVMp9yXVcu207bkNcqIgfT9jQ/Y08dJufh2lfGpFd6Nm+8HX7fb0q6joGqFz3JT6l0kCb0Aymo8UmDZVlmP78EbrE1te0CX2DztBUqwPTiR0AAEGttGuytVhdIcuQbWZHoJDIFyIjI1G/fv0yP1qtFu3atUN2djb27XO+Er9161ZIkoQ2bdqUuv3c3Fz07NkTWq0WGzZsuKY3bA8ePIjQ0NDrHsPwuqI1l2cFfvjhh/Hmm28iKMizsTXcDcCotUrQacq+KE3LVxqt8Ct6aEQEiEjPd9+gZRXKsEvu1zmVbnNsV6NCifG/wott12KTMfmzAjzd04CYEBFns0pW2GezJOw7Y4NWDbz1gD+yCmXM+roQ2YUyZvfzrPtyVqEMuwyEB7g2YhH+Ak6lu2800vPlkvn1d+YlrajLvrttphf9TpZlTP3CiAdaadEkVoWUq/QaMFtlfHXIilEdKmbsDcd+8L8yX1fbD+7y6PmFYFahhHe3W3BfBfR29NWxvhYdEtVYsduCrw5bcUcjNdLzZbzzk7nob9y4IEqpdUAZ+Sm9Dih9v/qKs5xfcbwCBJzKcJ+/9Hy55PL+AtJLeR3FbJPx2lYL7mykKnUcrNOZEj76zYpnunv/VRNflvOhbbRoWF2FEIOAA2ftmLfFhNQ8GVN7VewwFmWpTGX6dIYdH+024ZleFf+6jfOYuWnP867Wnl95fog4le4+8JVVIOHdbSbcd0vFjgtVHml5ZeyLq5YBN3VFmuv+W73bhNe+L0ChBagVIWLpw0EVNuZjRbTVjnPETb14eZmzRdcob/9kxpSeOsQGi/hwtwXDlhvx3Xh/hBQLBExaZ8TW4zaYbEDXuiq81Nd35//NKq3o9d3wQNfgT0SgiPRc9+Ugq0BSynSJdVQ4dckZDH/joQhMXJ6Bts+fh1oE9FoBCx+JQEKxsQaHdgpEwzgNQvxFHDhlwbyvspGaY8fUfqHeyiIAz66xrqUNdN6XuC4Tfp3XsGezJJzPlvD9ERvm9jPALgFzvjdhwppCLB/uf83buZ60X+lGXadeaf8ZG749YsWiByumPass9yW+pgoMh6BSw56b5vK9PTcVmup1S11PMAQh/rVjENQ6QLYj46NJMP2pxBmsF0/AlnEGoQNmIGPFk5DMBQjqOQ7qsDiogss3IypdgRODVIgGDRqgV69eGDVqFBYtWgSr1Yrx48fjgQcecMwMfO7cOXTv3h0rVqxA69atHQHAwsJCfPTRR8jNzUVubi4AJfioUqmwceNGXLp0CW3btoVer8emTZvw8ssv46mnnrruNHrUZevDDz/0ZDUHdwMwTh8Qg5n3xrl8t/GQBTM2Fjp+XjS4YsbzuBavbzYhMVKFu5qWflMsyUrvmtcG+DsmjXjWZsCENYWY0efaetNVFiv3WFBgBh7teG1BgE3HbCiwAPc0q5hXgSuDfLOM0auNSIwQMb5z5RiH40bpkKTG07fpMPMrI6asB7RqYGwnHX47Y4eXO0S62Pi7GTM2FKsDhviuDrgZWO0ynlynBG9n9nYfFLmUK2HUxyb0aqCukGC3Lz1cbBiHetVU0KiAGV+ZMLmH7oZMhAFU3jJ9KVfCqBX56NVYi/taeT9gtvGgGTM2OF/pWzQ00Ot/40r5JhmjV+YhMUqF8d18O9ZncRsPmjHjy3zHz4uGVcxEWpf1baZF+yQN0vIkLP3FiCc/ycPHjwZD9x+6Jrmay6NzjO6gxe0NlHpr9l16dH6jAN/9acUDLZ1t9tTbdRjfWYt/M2TM22rGnB/MmNGbgcDy2PhbAWasyXL8vOhR92OBecOb3+Ygzyjhw8ciEeovYvNhIyYuS8dHT0ShXoxynB/u6qxf6sVolbp+TRYm9w0pV12/8ZAVMzY6X2deNLhyj08mycqbHHP66R3jk790lwED3i/AP+l21C7nmOWV1YlLdoz7xIhxnXXokOT9t5Oo/GRTHs7P6gBR5w99g84Iu/9l2NL+hen4L4DdhtS3hyBi+FuosfAMZLsNpj9/ROGhH+D2VTyiSmjVqlUYP348unfvDlEUMWDAACxYsMDxe6vViuPHj6OwULkn2L9/P/bs2QMASEpKctnWqVOnULNmTWg0Grz99tuYOHEiZFlGUlIS5s2bh1GjRl13+jyuGX/77TesWbMGZ86cgcXiOg7H+vXry1x36tSpmDRpkst32i+7l1iuaz0NkmOdDbml6MFKRtEshpel50slZk66LNRPgEpEiQFr0/MlRBQ9jYkMEGC1A7lGyaU3YEaxZfacsuLEJQnf/5kNAJCLLjjbvZKL0R11eKKbAZEBAqKDRJdZYxMjVZBl4GKudNWZwkpNvwBkXPGUKL1ALnXMqogAoWR+C4rnVyzKn4yowOLLyI79uOeUHQdT7Ej+X57Ldu59vwB9kjWY28/1huqz/RZ0qauusHG0HPuhQALg3I9X3w/u9tv1NyD5ZhkjVxXCXyfgrfsN0FTAa2W+OtbX6uH2Ogxvp0VqnvK64LlsCfO2mBFfgWNqda2vdZnt1FLUGaBEHVBGfkqvA0rfr77iLOdXlIH80sttRIBQcvkCGRFXPJ222mVMXG/G+RwZy4bo3fYCvJQnYdhHJjSPU+HFOysm0F2ZynlynAo2SZkd+UbdDFXGMn0pV8KwpXloHq/Gi3dVzE1t1wZaJMcXz7dy/N2259Wvlu8rzw+pRL7zzTJGLs+Dv1bAWw8GVEid7SllX4Q4fnbZF0FX7gv3l2ml74uSdUWgXkSgHqgZoULTeDXavJSJTX9a0Kep94O9FdFWRxb9m1FwxfmdL6NBNdFlmaRI59/QqgXEhwi4cMWM4JEBIiIDgNoRQLABGLzMiLEdtW4nQKNr07WxAckJzjbDUa/l2REVXKwc5EloEOv+4VKov6iU6TzXXlTpeXZEFJ0XZ9JtWLU9HxunVEOd6sp26sdqse8fM1b/ko9Z94W53XZygk6p6zNsqB3t+cOtrvXUSI51Prix2C+fu9fe9lxLG+i8L5FdegNmXOc1bGSgALUIlwkKE4vOkQs5MmpfZ6y2MrXfpfk71Y6HVxTivpYajO1ccT3AfX1fUlnY8zIg221QBUW6fK8KioI951LpK8oybKn/AAAsZw9DU70egntPVoKAACynD+L8rA4QDEEQVBpI+Rmo/vxWmP89UGF5IfKmsLAwrF69utTf16xZE7LsrA+6dOni8rM7vXr1Qq9evbySPo+ueD755BO0b98eR48exeeffw6r1YojR45g69atCA4Ovur6bgdgdPMqcIBOQEK4yvFJihQRGSBg1z/O1wLyTTIOnbOjWbz7C2WtWkCj6iqXdSRJxu5TNsc6jWLU0KiAXaecy/yTbsf5HNmxzIL7/fHF2EB8Pkb5/O9uJQj20SMBGNxaaWRa1FAjNU9CQbGJEv7NkCAKQLUgzy4utWoBjWJEl7RJkozd/9jQrJQBYZvFq7DrlOtF1M6TdsfycaGCsh9PXbEfU5zLPH+HHl+M8cfnRZ/3ip52zhtowMRuro1qSpaEPafsGNC84noNaVUCGlUXXfIlyTJ2n7KjWZz7fdssTuWSRwDYWcZ+K02+WcaIjwqhUQl45wFDibHxvMVXx/p6CIIS6NZrBHx92IrqQQIaVq+4G6cSdUBUKXVAiq3sOiDGTR3wj7XUdXyl1HL+rx3NYksr5yJ2/XtFGbjivLgcADydKeHDwXqE+rkJAOZKGLbShEbVRLzcVwuxgp62VqZyfuyiUj9f+TpPRapsZfpyALBRjAov9/eDWEFde0vmW6Xk+6TzNd5rz7dzHXf5zjfJGLEsFxoV8M6QwErX463UffFP8X0hKfuilInFlH2hdtl/kiRj90krmtUouy2W4QxeeFtFtNVxIZfPb+c2881F135FyzSOUUGrgsuwCVa7jHM5MmKCSz/+l3sQWirXyBD/OQF6EQmRGscnqZoakUEidv3lnHk53yTh0GkzmtV0H5jRqgU0itNi11/O4YIkScbuE851jBbl+F5ZTYmCgLLmajt2zqLU9YHle9ijnLui4+O4L7mOtuda2sBGMSpoxFLuS67j2q1FvBo2CTiT6Twv/i06R8o6L0pTmdpvd/5KteOh5YW4p6kGE7tXbO9eX96XVCp2KyynD0LfoIvzO0GAvkFnmE/+eu3bEUXHWIDFycZcSPkZUEclQluzOQoPfl3+NBORZz0BX375ZcyfPx/jxo1DYGAg3nzzTdSqVQujR49G9erVvZ1GB0EQMKytDot+NqNmuAqxoSIWbDUiKlBEj/rOi97hy/LRo4EGQ9ooFw3D2+vw7OeFaByrRnKsCst3mWG0AP2bK5VNoF7AgOZazP3OiGCDgACdgJe+MaJZvMpxY1EjzLWCzipUGtHECNHRe7BPEy3e/cmE574oxONd9cgqlPHKD0YMaK4t16vAw9vp8OznRjSOUSnp322B0Sqjf1HQbcp6I6KCBEzuoTR4Q9toMWxZIZbuNKNLHTW+/sOKI+fteLFo3BtlP2qV/RgmFu1HM6ICBfSor+Q3JsS1AfPTKvmt4WZA8XUHLIgMFNCpTsUGVIa30+LZL0zKfogRsXyPVdkPRa8gT/lCKQuTu+uc+2F5IZbusqBLHRW+/sOGI+clvNjHeWGQbZRxIUdCatEYVJdvICICBEQGiI4AoNEKvNpPj3yz7JgNN8xPgMrLN8y+ONYAcD5bQo5RxoUcZYyToxeUi5oaYSL8i3qMLdlhRockNUQB2HTUhsW/WDB/oMHr+6AsgiBgWDs9Fv1oKsqPCgu2FNUBDYrVAR/mKXVAW2U/DG+vx7PrC9A4VoXkWDWW7zIpdUAL5wVHWp6E9HwJZ4rKwIlLdvjrJFQPFhHid+OCRMPbaPDsBjMaVxeVMnC5nDctKgNfKsdvcjcl7UNv0WDYShOW7raiS5IKXx8pKudFr/ta7TImrDPjzwsSFj2gg12WHWNsBRsEaFWCIwAYEyxgSg8tMgtlKKEC51N6r+bRB+X8wFkbDqXY0aaWGv5aZXD12d+Z0DdZ4zJw+N+pdljtQI5RRoFFdpwLpfVOKy9flulLuRKGLclDTIiIKb38kFlQ7LhXcK8oQRAwrH1Rvi+35458O/MwfGkuejTUOvN9qx7PritA4xg1kuPUWL6zKN8tlfJ+OQBotAKvPujvWmf7O+vs0xl2FFpkpOfLMNlkHL2g3JglRqpu2KvhlwmCgGG3GrBom9G5LzYXltwXS3KUfdFOeQip7It8pQw49oXs2BdnM+345rAFtyZpEOYv4GKOhMU/G6FTC+hct+KGtPB2Wy0IAoa10WDR9qLzO0TAgh8tLud3gE7AA600WPijBdWCRMQEi1i6S3lLpVdD5e/+9JcN6QUymsQos4L/nSrh1c1mtIhXIS6k8vUC1Pn5IzIh0fFzRFxNxDVIRkF2FrIunPVhyq5OEAQM6xSIRT/komakBrFhKiz4JhdRwSr0aOJ8k2T426nokWzAkI5KF7DhXQLx7OoMNI7XIrmGFst/yoPRIqF/G2X8utrRGiREqDFjTRaeuTsYIf4qbD5ciJ0nTFg0SunWduCUGYdOW9Cmjg7+OhEH/zVj9hfZ6NvKD8Febsuv9Rpr+PIC9KivwZA2ynl3tTYwUC9gQAsN5n5vKnZfYkKzOJXLA4/TGVKxesx57ZYYKUKrFtC+tgoNq4t47ksjnuulhyQD//vahPa1VS69A69HZb1OPXHJjuHLC9EhSY3h7bSOsVZVIhBWQQ/6fHFfAijjKKbny47grtK2C0rbfgMm9rpSzg9vIXLEIpj/PQDLqd8Q1OMxCDo/5O34SEn7iPdgyzqP7PXKUGDBvSfB/O8B2FJPQdBoYWjSEwFtH0DGRxMd2/RrdQ+kvHTYMlKgiWuI8EFzUXjgK5iObL3h+SO6GXkUtTl58iTuvPNOAIBWq0VBQQEEQcDEiRPRrVu3EuP9edPIDjoYrTKmbyxErklGyxpqLB7i7/Kk/0yWHVmFzsatd2MtMgtkLNxqRFq+0r188VB/l+7aU3sZIArAhE+VWcY6JGkw/c7rG0PIXydg6bAAvPSNEfe+n4cQg4BejbR4spxPo3o31ijp32YuSr+IxUP8HOk/nyNBKDawZ4saarw2wIA3tpoxf4vSqL71gAF1o537ZOStWhgtMqZvNBXtRxUWD/G77h4TkiTj84NW9GumqfBgUO9GRfvhx6L9EC1i8YPF94MMQXA+7WwRr8Jr/fV4Y5sZ87fKyn6434C6Uc79sPW4Dc9tcD6pnrRO+f+4Tlo83kWHIxfs+L1oVtaebznHswKAzU/4Iy7Eu3n21bFesM2ML3539ijp956S1+UP+aFN0UxmP/9lw6KfzbDYgfrRKrw9yIBOdW78mHEjOxbVARuK1QHDAlzrgEwJWcVmv+vdRIvMAgkLt5iQVvS64eJhAS51wCd7zXh7m7MsDFmijNn1cj8/9G9x4yYV6N1IjcxCGQt/siKtwKKU80F6x+siJcpAvAqv3aPDGz9aMH+bBTXDBLx1nw51o5RlLuXJ2HpCuVi+Z7HJ5W8tH6JHm5oq7Dhlx+ksGaezZHReYHRZ5ti06x88/Kp59EE516oEfPOHDW/9qJThuBARD7XT4uF2rsGQR1cV4nyx1wcvnwvHZlbcuG2+KtM7/rbidKaE05kSOr+a45KmY//z7gD67ozsqFeO2ZcFznw/FFgy38UmuendRKeUnS1GZ74fCnTk+8h5G34vmmmx53zXPG2eHIy4UKXMTPu8AHv/dfbI6Pd2bollbiTHvvgiX9kXCRosHh5UdhlI1hWVASPS8pRXhxcPd+4LrVrAvn+tWLHDiFyTMjh/q5pqfDw6uMRA/d5UEW31yPZaGC3A9K+Knd+D/Vx65j/dQweVAEz5wgSTVUbTWBWWDTU4gvw6NbB2vwVzvpdgsQPVggT0rK/BqA6Vc4zfhMYtMfmjTY6f73vuVQDAzvUrsPzZ6x//50Yb2T1QKdOfZiLXKKFlbR0Wj450LdPpNmQVe1W0dws/ZBbYsfDbHKTl2tEgVovFoyMRUdSDT6MS8N7oCLy+MQdjF6ej0CKjRoQacx4MQ+eGyjW7Vi3gmwOFeOu7HKWuD1Phoc6BLuMEejWf13CNpZy7xfJ5lTYQAKberocomJT7ErsyQdv0O13vJ6ZtMGLvaWdPtMvt1eYJAYgLFSCKAt4d5IeXvjVhyIcF8NMI6FhHjSk9Pb8vqazXqd//aUVmoYwNh6zYcMi5XEywgK0TK+bY++K+BAA++c2Kt392DsU1ZLly3fbyXXpHAPJGKty7HpmBEQi95zmogqJhOXsYl+YPgFQ0WYg6LA6QnftB0PkjfMg8qEJjIFtNsF44gbQPRqFwr3M4MVVwNYTd/3LRa8UXkb/zE2RvnHvD83bT48QgVZYgX+3lYzfi4uLw7bffokmTJkhOTsbUqVMxaNAg7Nq1C7169UJOTs7VN3IF+ZPSp0u+qcnS1Ze5GZX13sbN7Ab2mqtUVP/hVx3Kw2y5+jI3I3UVPd5VtZxX1YG6q2q+q2i9NmbWYV8nwScWvdnR10nwjezrv5eh/7Aqel9yeusJXyfBJ2ouyfV1EioF+fATvk6CzwlNFlx9oZuQRz0BO3XqhE2bNqFJkyYYOHAgJkyYgK1bt2LTpk3o3r3kBB9ERERERERERETkOx4FAd966y2YTEr35Oeffx4ajQY7d+7EgAEDMG3aNK8mkIiIiIiIiIiIiMrnuoOANpsNX331FW6//XYAgCiKePbZZ72eMCIiIiIiIiIiIvKO6w4CqtVqjBkzBkePHq2I9BARERERERERUUXhxCBVlkdHvnXr1jh48KCXk0JEREREREREREQVwaMxAR977DFMmjQJZ8+eRcuWLeHv7+/y++TkZK8kjoiIiIiIiIiIiMrPoyDgAw88AAB44gnntNKCIECWZQiCALvd7p3UERERERERERERUbl5FAQ8deqUt9NBREREREREREREFcSjIGBCQoK300FERERERERERBWNE4NUWR4FAVesWFHm74cNG+ZRYoiIiIiIiIiIiMj7PAoCTpgwweVnq9WKwsJCaLVa+Pn5MQhIRERERERERERUiXjUBzQrK8vlk5+fj+PHj6NDhw74+OOPvZ1GIiIiIiIiIiIiKgevvQhep04dzJkzp0QvQSIiIiIiIiIiIvItr44GqVarcf78eW9ukoiIiIiIiIiIiMrJozEBN2zY4PKzLMu4cOEC3nrrLdx6661eSRgREREREREREXkZZweusjwKAt5zzz0uPwuCgMjISHTr1g2vv/66N9JFREREREREREREXuJREFCSJG+ng4iIiIiIiIiIiCrINQcBJ02adM0bnTdvnkeJISIiIiIiIiIiIu+75iDggQMHXH7ev38/bDYb6tWrBwA4ceIEVCoVWrZs6d0UEhERERERERERUblccxBw27Ztjv/PmzcPgYGBWL58OUJDQwEAWVlZePjhh9GxY0fvp5KIiIiIiIiIiMpPUPk6BeQjHk0J8/rrr2P27NmOACAAhIaG4qWXXuLEIERERERERERERJWMR0HA3NxcpKWllfg+LS0NeXl55U4UEREREREREREReY9HQcB+/frh4Ycfxvr165GSkoKUlBSsW7cOI0aMQP/+/b2dRiIiIiIiIiIiIiqHax4TsLhFixbhqaeewoMPPgir1apsSK3GiBEj8Oqrr3o1gURERERERERERFQ+HgUB/fz88M477+DVV1/FyZMnAQCJiYnw9/f3auKIiIiIiIiIiMiLBI9eCqWbgEdBwMv8/f2RnJzsrbQQERERERERERFRBWD4l4iIiIiIiIiI6CbHICAREREREREREdFNjkFAIiIiIiIiIiKim1y5xgQkIiIiIiIiIqL/EE4MUmXxyBMREREREREREd3kGAQkIiIiIiIiIiK6yTEISEREREREREREdJOrPGMCypKvU+AbkuzrFPiGSuXrFPiG3e7rFPiG3errFPiGTuvrFPiGWEWfL5ktvk6Bb8hVsx0zn8/ydRJ8Qte8jq+T4BOL3uzo6yT4xJgJ232dBJ+oqscbRqOvU+AT9vPpvk6CTyT0a+nrJBCRD1SeICAREREREREREVUsTgxSZfHIExERERERERER3eQYBCQiIiIiIiIiIrrJMQhIRERERERERER0k2MQkIiIiIiIiIiI6CbHiUGIiIiIiIiIiKoKTgxSZXkcBNyyZQu2bNmC1NRUSJLk8rulS5eWO2FERERERERERETkHR4FAWfNmoUXX3wRrVq1QvXq1SEIgrfTRURERERERERERF7iURBw0aJFWLZsGYYOHert9BAREREREREREZGXefQiuMViQfv27b2dFiIiIiIiIiIiIqoAHgUBR44cidWrV3s7LUREREREREREVJEEkZ8qyqPXgU0mE95//31s3rwZycnJ0Gg0Lr+fN2+eVxJHRERERERERERE5edREPDQoUNo1qwZAOCPP/5w+R0nCSEiIiIiIiIiIqpcPAoCbtu2zdvpICIiIiIiIiIiogpSdV+EJiIiIiIiIiIiqiI86gnYr18/t6/9CoIAvV6PpKQkPPjgg6hXr165E0hERERERERERF5ShSfGqOo8OvLBwcHYunUr9u/fD0EQIAgCDhw4gK1bt8Jms+HTTz9F06ZNsWPHDm+nl4iIiIiIiIiIiK6TRz0Bq1WrhgcffBBvvfUWRFGJI0qShAkTJiAwMBCffPIJxowZgylTpuCXX37xaoKJiIiIiIiIiIjo+njUE3DJkiV48sknHQFAABBFEY8//jjef/99CIKA8ePHl5g5mIiIiIiIiIiIiG48j4KANpsNx44dK/H9sWPHYLfbAQB6vd7tuIFERERERERERER0Y3n0OvDQoUMxYsQIPPfcc7jlllsAAHv37sXLL7+MYcOGAQB++uknNGrUyHspJSIiIiIiIiKi8uHEIFWWR0HA+fPnIzo6Gq+88gouXboEAIiOjsbEiRMxZcoUAEDPnj3Rq1cv76WUiIiIiIiIiIiIPOJREFClUuH555/H888/j9zcXABAUFCQyzI1atQof+qIiIiIiIiIiIio3DwKAhZ3ZfCPiIiIiIiIiIiIKpdrDgK2aNECW7ZsQWhoKJo3b17mpB/79+/3SuKIiIiIiIiIiIio/K45CHj33XdDp9MBAO65556KSg8RERERERERERF52TUHAWfMmOH2/0RERERERERE9B/B2YGrLB55IiIiIiIiIiKim5xHE4PY7XbMnz8fa9aswZkzZ2CxWFx+n5mZ6ZXEERERERERERERUfl51BNw1qxZmDdvHu6//37k5ORg0qRJ6N+/P0RRxMyZM72cRCIiIiIiIiIiIioPj4KAq1atwuLFizF58mSo1WoMGjQIH3zwAaZPn47du3d7O41ERERERERERERUDh69Dnzx4kU0adIEABAQEICcnBwAQJ8+ffDCCy94L3VEREREREREROQ9nBikyvIoCBgXF4cLFy6gRo0aSExMxA8//IAWLVpg79690Ol03k6jw6pfLViyw4z0fBn1q4mYdocByXGqUpf/7ogVb24141y2hIRwEU/10KFzXY3j97IsY+E2M9butyLXJKNFvAoz+uhRM7zkNi02GfctLsCxSxI+H+2PBtWdy2z/24a3tpnxV5odOrWAVgkqTOmpR1yod06sVXstWLLTouQ7WsS0O/RIji0j339a8eY2izPf3XXoXMd5qGVZxsIfLVh7oFi+e+tRM9w1vT+esOGdn804nipBpwZuSVDj7fsNjt8fPmfH61vMOHLBDkEAmsSo8HQPHepXKz1t5aUcMxPW7rMoaa+hxow+BrfHrLhVe8xYstNUtA9VmNbbgOQ45z4xW2XM/d6Ir/+wwmqXcWuiBjP6GBAR4Nwnu/6x4s2tJpy4ZIefVsA9TbV4srseapXg1TxWxuOdVSjj6c+NOH5JQrZRRri/gG711JjUTYcAnXfzX9yqvRYs2WV17oteuqvsCxve/NGMc9kyEsJEPNVd67Ivfjhqwyf7rThywY4cI/D5KAMaFCuvKdkSeiwsdLvtNwbo0auhR1XmdZNlGQu3mrD2N7OznN/ldw3l3IQlv5iRni+hfjUVpt3p51LOP91rxleHLPjzgg0FZuDX54IRZHAtB91ez8H5bMnlu0m3GfBoJ733MlgKWZaxcIsRa/ealHwnaDDjLn/UjLhKvnebsGS7sSjfakzr44fkeKWuzy6UsHCLETv+tuBCtoQwfxHdG2oxoYcBgfqSdXRWoYR7FubgUq6EX6eFltg/N4K364Afjlrxyb5i5f5RP5dyX1l483y32mW8uc2Cn/62IyVbQoBOQPtaKkzqrkV0YOW66FU1Hwp161GAfyTk1KOwbp4J+eIht8sK4XWg7jARYrXGEILjYN3yP9j3fVhywYBoaDpPgVi7M6A2QM4+Deu3z0C+eLiCc3PtZFnGwq+zsXZnPnKNElrU1mHG/eGoGaUpdZ29f5uwZHMOjpyxIC3XjrdGRaJHU3+XZX44WIBPfsnDkTMW5BRK+PzZ6mgQV3HXplcjyzIWfpuLtbvzkWuU0aKWFjMGhqJmZOn5BIBV2/OwZGse0vPsqB+jxbQBIUhOcOYjLdeOVzdkY+dxEwrMMmpFqTH6tiDc3tTPsUy3WedxPsvust1JfYLxaI8g72bSi+q06oCeIyehRqPmCImOwTuPDcTvmzf4OlnXzJfH+8hZC17fmI3DZywQRQE9mxrw7D0h8NdVfJ0nyzIWbi5U2m9jUft9T8DV2+9dRiz5uVj7fZe/o/0GgOmf52PX3xak5krw0wloXkODp3r5oXaUUtdnFUh4+tM8HL9oR3ahhPAAEd0aaDHpdj8EuGnjfWH1IeDD/UB6IVAvAniuE5Bczf2yf2cAC/cAf6YC5/OAKR2BYc1uaHKviSzLWPh9HtbuLlTq71pazBgQgpqRZV8nr/qlAEt+zC8q5xpM6xeM5BpaAEBKpg09/i/V7XpvDAtFr6YG5XivysLxC1ZkFxQd78Z6TOodVGmON9F/gUdnS79+/bBlyxYAwOOPP44XXngBderUwbBhw/DII494NYGXffOHFXO+N2FcFx3Wj/ZHvWgVRn5UgIx8ye3y+8/YMPkzI+5tocHnY/zRo74a4z8x4sQl58XQBzssWLnHgpl99Fgz0h8GrYCRKwthtsoltvfqJjOi3Nw4pGRJGPdxIdrUUuGLMQH4YIgfsgplPPGp+0DCdef7iBVzfjBjXGcd1j/qh3rVVBi5qhAZBaXk+6wdk9eZcG9zDT5/1A896qkx/lMjTqQWy/dOC1b+asHMO3VYM8IPBo2AkasKYbY58/39USumfGFE/2YafDHaH6sf9kOfxs6KvcAiY+TqQlQPFvDpCH+sGu4Hf52AkauMsNpL7j9v+eAXM1buMWNmXz+sGRUIgwYYubLA7TG77Js/LJjzvRHjuuixfnSgsg9Xupad2d8Zse2EFW/e54cVDwcgNU/C458UOH5/7KIdj35UgI5JGnw+JhDzBvpj63ErXt9s8mr+KuvxFgWgez013nnAgO/G+WP2XXrs+seOGV97N/8l9sUmC8Z10mL9KD/UixYxcrWx7H2x3oR7m2nw+Sg/9Kinwvg1Jpd9YbTKaBmvwlPd3d8QVg8SsH2in8vn8c5a+GmBjkk3LmjywXYzVu42Y+ZdflgzOlCpm5bnl13OD1sw51sjxnXVY/3YIKXsLM93Kecmq4yOdTQY3clQ6nYA4Iluemx/JtjxGdL2xtxAf7DdhJW7TJh5dwDWjA1Wzu9luWXn+5AZc74pwLhuBqwfF6zke1meI9+peRJS8yQ808sfG58IwewBAdh+woLn1xe43d609fmo58MAWUXUAUYryiz3lYG3z3eTFfjzooTHOmqwbqQfFg7U41SGhMc+rbg6yxNi/Tuh7vocbDsWwLK8L6S0o9DetxzwC3e/gsYAOecMrD+9Ajnf/c0SdEHQDV4LWbLBsvZhWJb2hG3b/wGmnIrLiAc+2JyLlT/lYuYD4VjzVHWlnnv7EsxW98ccAIxmCfVjtZh+f1jpy1hktEzU46l7Qisi2dftgy15WPlzHmYODMOaiVFKPhellV2v7S/EnC+yMa5XENY/VQ31YjUYuSgNGXnO83rKqgycSrXhnZER2PBMNdyWbMDEZRn4M8V1sr4n7gjC9hdjHJ8hHQMqLK/eoPXzR8qxQ/j4xQm+TopHfHW8L+XY8ci7aagRocanE6PxwZgI/H3Riqmrb8xkjR/8bMTKnSbMvCcAax4LUfK9NOfq7ffXBRjX3Q/rx4egXnUVRi7NdbluaRSrxsv3BuLrSaH44OFgyABGLM2FXVK2K4pA94ZavDMsEN9NDsXsewOw66QVM75w38bfaN+eAF7ZDjzWGlj7gBIEHL0ByCjlNtFoA+KDgIntgQg/98tUBh9sy8fK7QWYeW8w1kyIhEErYuT7GWUf7wNGzNmQg3E9A7F+YiTqxWgw8v0MRzmvHqLC9hnRLp/Hbw+En05Ax/rK9YsoAN0b6/HOI2H47tkozH4gFLtOWDDjs8rVvhFVdh4FAefMmYPnnnsOAHD//ffj559/xtixY/HZZ59hzpw5Xk3gZct2mTGwhQYDmmuRFKXCrD566DUC1h2wul1+5R4LOiSpMeJWHRIjVZjQTY+G1VVY9avSWMqyjBW7LRjTSYfu9TWoV02Fuf0MSM2TsfmYzWVbP/9lxY6TNjzTs+QN1B/n7ZBk4MluOtQIE9EoRoVH2mtx9KLklWDYsl0WJd/NNEiKVGHWnbpryLcKI9prlXx31aFhdRGr9lqd+d5jxZiOOnSvp0G9aBXm3qN3ybdNkvHyd2Y8fZsOD7TSola4iKRIFe5o5Hwy90+6hBwj8EQXHWpHiKgTpcK4TlqkF8g4n1MxQUDlmJkxppPeecz6+yM1T8LmY+73BwAs22nGwJZaDGiuKyo7Bug1wLoDSlnIM8lYd8CCKbcb0La2Bo1j1Jh9jx8OnLXj4Flln3zzhwX1olUY10WPhHAVWtdU46meBqz+1Yx8s/fyW1mPd7BBwKBWWjSJUSE2RES72moMaqXBvjN2t+nyyr7YbcXA5pf3hejcFwdtbpdf+au12L4QS+wLALg7WYNxnbRoV8t9kEclCogMEF0+m4/ZcEdDNfy1FdfjsThZlrFilwljOuvRvYEW9aqpMXdAUTk/WlY5N2FgKx0GtCgq5339lHK+33lD+FB7PR7tpEfT+LKDXP46AZGBouPjdwPyLssyVuwwYkwXA7o3LMr3wICifFtKXW/ZjqJ8t9QjKUqNWXf7K/neZwYA1I1WY+GDgejWQIsa4Sq0TdRg4m1+2HbMAtsVdfTHe5QeiI90KDtIWpG8XQcAReW+sw7tat+Ynqye8Pb5HqgXsHSIAXc00qB2hIhmcSq8cIcORy5IOJ9TepDpRlO3GgH7oU9h/+MzyBl/w/b9NMBqhKrJQLfLyxcPwfbjHEjHvgLs7s8LdZsxkHMvwPbtM5AvHoKckwLp318gZ5+pyKxcF1mWsWJbLsbcHoLuyX6oF6vF3GGRSM2xYfPvpT9E7dTID0/2DcVtV/T+K+7u1gEYd0cI2tWr+N7LVyPLMlb8nIcxPYPQvYkB9WK0mDs4HKk5dmw+bCx1vWU/5mFguwAMaBOApGoazBoYCr1WxLo9zsDGwVMWDOkYgOQEHeIj1BjbMxiBBhFHzrqWC3+diMgglePjdwN6hZXHkZ+/x5dvzMTBTf+d3n+X+fJ4/3jECLUITL83FLWjNWhSQ4eZA0Pxw+9GnE4r/drBa/neYcSYrgZ0b6hDvepqzL2vqP3+s4z2e7sRA2/RY0ArPZKi1Zh1TwD0WgHrfnM+rLm/tR631NIgLlSFRrFqPHmbHy7kSDiXpdTjwQYRg9oa0CROg9hQFdolaTGojR77/q3YPF+r5QeBexsB/RoCSWHAjK6AXg2s/9P98k2igac6AL3rAtrK12EfwOVyXoAxPQLRvbEB9WI0mDsoBKm5dmz+o/QHbct+zsfAtn4Y0NpPKecDgpV2/lelzleJgktdFRmkwubDRtzR1ODozRrsJ2JQe380idciNkyNdnV1GHSrH/b9Y74heSe6WXjlSqBdu3aYNGkS+vbt643NlWCxyThyXkL7YjcwoiigXW01Dqa4D0IcPGtH+9quteetSSrH8ilZMtLyZZdtBuoFJMepXLaZni/hhQ0mzO1ngF5T8ka4cYwKggCsP2iFXZKRZ5Kx4Xcr2tVWQVPO10QtdhlHLkhoXyxgIQoC2tVS4WCK+5uYgyl2tK/leqN3a6JzP6VkX863c5uBegHJsc58/3lBwqU8GYIgoN/7Beg4Lx+jVhW69CypFS4ixCDgswNWWOwyTFYZ6w5akRghIjakYgIGKVmS+2MWq3IE665ksck4csHuvuwUrXPkvA1WO1yWqR2pQkyw4FjGYgN0V9w/69WA2aas7w2V+Xhf6VKehE3HbLgloWKuUMreF6Wc8yl2l+UB4Nbape+7a/HHBTuOXpIwoFnZr/F4k6OcJ15ZN6nLLufn3ZTzRE2p65Rl8XYT2rycjX5v52LJL6YSwbKK4My3c18H6kUl32fKyrcN7ZO0ju9EUUC7JC0Onin9BiDPJCNAJ7i8yv93qg3vbC3E3HsDINyYeG8JFVEH/BfcqPM9zyRDABCk99EBvpKogVCtMaR/dxT7UoZ0egfEmOaebzapO6RLh6G56y3oxv0K7UMboUq+v/zp9aKUDBvScu1oX98ZqAs0iEiuqcPBf2+em7mUDDvSciW0r3tFPhNKz6fFJuNIigXt6zofPIuigHZ1XddpVkuLbw4UIrvADkmS8fX+QlhsMlonuT6wXrwlF22eO4d+r17Ekq25N6Q+r6p8ebwtNhkatQBRdNZvl+9b9v1TeiDOG1KyJKTlyS5tcaBeRHK8utS22Nl+O9t8x3VLKW1+oUXG+n0mxIWKqBbs/hb2Uq4dm46YcUutG3fdVhqLXXmtt1288ztRANrGA79f9F26yisl0460PMmlzAYaRCTX0OLgafdlTSnnVrSv46acn3ZfRv44a8HR8zYMaF16l8hLOXZsOmzCLYmV900HosrI424Bx48fx8KFC3H06FEAQIMGDfD444+jXr16V13XbDbDbHZtDLVWCTqN+wo9q1CGXQbCA1wv3CP8BZxKd3+DkJ4vIzxAvGJ5Een5ysVPWlFXc3fbTC/6nSzLmPqFEQ+00qJJrAopWSVvLuJCRSwZ6oeJa42YsdEEuww0i1Ph/cHl78PtyLf/lfm4Wr7d5elyvpV/w/2vWCbAuczZony+/ZMZU3rqEBss4sPdFgxbbsR34/0RYhAQoBOw4iEDxn9qxLvblQo/IUzEB4MNUIsVc4PlSPuVxzXAeVyvlFUowy65X+dUus2xXY0KJcb+Ci+23Q5JaqzYbcZXhy24o5EG6fky3vnJ5JKu8qrMx/uySeuM2HrcBpMN6FpXhZf6VkxPi7LPefc3+en5spt8ikgv8DxIu+6AFYkRAlpcpeecN5VazovVTVcqvZyXXnZKM7StDg1jVAgxCDhwxo55m4xIzZMw9Y6KfS8lLe9ynezu/L5avkuW71NppdQJBRLe/dGI+25xll2LTcbkT/Px9B3+iAlR4Wymb3qKVUQd8F9wI853s03Ga1ssuLOxukLHMb0ufqEQRDXkwnSXr+WCdIhhiR5vVgipAVWzwbDvXQLL7ncgVk+GuvsMyHYrpCPry5tqr0jLVcpzeKBr3RoRqEJ67n8ngH01aXml5VMsNZ9ZBZJSr7nZN6cuOcv3Gw9FYOLyDLR9/jzUIqDXClj4SAQSio09N7RTIBrGaRDiL+LAKQvmfZWN1Bw7pvarHK9K32x8ebzb1tFj7hfZWLI1F0M7BcJokfH6V8orkmkVfE6V2X7nldZ+S+6vWwJFnLqi5+LqXUa89l0BCi1ArUgVlo4IhlbtWo9P+jgXW49aYLICXRto8VJ/37/2nm2E0rZdcfkU7gecyvJNmrwhLbfoeAeWPHbXXc4DRJxKdR84XPdrIRKj1WhRS1vid5NWZmHrERNMVhldG+rw0n0hHuSEODFI1eXRkV+3bh0aN26Mffv2oWnTpmjatCn279+Pxo0bY926dVddf/bs2QgODnb5zP7ygidJqVAr91hQYAYe7Viy8rksLU/pKXhPUw3WPuqPlcP9oFEBE9YUQpb/OzdhxRUNs4HRHbS4vYEGjWNUmH2XHoKgDEAPKGOLTdtgQvN4FT59xA+rH/ZDnSgRYz42wlTGeBDXY+MhC1r8X7bjY/PhG1wdkjR4uqceMzcWIvl/Oei1IBed6igXXhUU87xhruV4Xzb1dmWcsnfuN+Bslow5P9w8PTauZLLK+OoPW4X3Atz4uxkt/pfl+Pi6l8bDt+rRppYG9aqp8UBrHab0MmDVbjMsNu+ma+NBM1rMynB8bsT5nW+SMHpFHhIjVRjf3fnK7+s/FCIxUoW7mvFJ8s3Iapfx5GfKQ5uZvavAMRYEyJf+gG37a5BT/4T9909gP/QJ1M0e9FmSNu7NR4tJpx0f280T53Ox8bcCtHgmxfGpyPr8zW9zkGeU8OFjkfhscjSGdwnExGXpOH7eeUP9cNdAtKmjR70YLR64NQBT7g7Bqu35Xq/Pq6rKdLzrVNdg9uAwfLgtD82fSUGHF84hLkyNiEDR69epGw+Y0GJGuuNT0edz3+Y6rH88FCsfDUbNCBWeXF1yrOCpfQKwfnwI3hkaiLMZdsz5unKMCXgz2LivEC2mXnB8bFLF1x8mq4yv9htL7QU49e4grJ8YgXceDlOO9waOCUh0PTzqCfjMM89g6tSpePHFF12+nzFjBp555hkMGDCgzPWnTp2KSZMmuXyn/aJrqcuH+glQCUDGFT0b0gtkl9lbi4sIEEpMGpJeICGiqKdBZNF6GfkyogJdt3l51sQ9p+w4mGJH8v/yXLZz7/sF6JOswdx+Bqzea0GgHni6p7NXyav9RXSZn4/fU+xoFu/5GEyOfBdIAJxPTq6eb3f76XK+lX8zCq7Id76MBtVEl2WSIp1/Q6sWEB8i4ELReH9f/WHFuRwZn4zQQyx6d+61/nq0eSUfW47bcGfj8gdOutbTIDnWmUhL0UVGRr7kMklLer5U6kyXoX4CVCJKloV8yWWfWO1ArlFy6Q2YUWwZAHi4vR7D2+mQmicj2CDgXLaEeZtNiPfSLNCV+XhfpoyTB9SOAIINwOBlRoztqHU7aU55lH3Ou7+ajQgQkFFwxfL5EiL8Pbv6/f6oDSYrcE9yxQYBu9bXuszgayl68F+inBerm65Uejkvvexcq+Q4NWyS8rpP7Ujv9Yjs2kCL5PgQx8+Xb0oz8iVEBV1xfld3X486833lcS9ZTvLNMkYuz4O/TsBbgwNdhmvYc9KKE5fs+P6FDADA5ec37V7OwujOBjzR48aMzl0RdcB/QUWe71a7jInrTDifI2PZUEPl6QUIAIVZkCUbBL8IFM+J4B8BuSDN8+3mp0HK+NvlKznjJIS6vTzfZjl1beKH5JrOAKzjfM+zIyrYeX6n59nRIK70B6+VXdfGBiQnONPvqM/z7IgKLnZO50loEOu+bQn1F5V6Lc81spKeZ0dEUd14Jt2GVdvzsXFKNdSprmynfqwW+/4xY/Uv+Zh1n/uJU5ITdEp9nmFD7Wjfvy75X1fZjnfflv7o29If6Xl2GLQCBCjjDcZHeHc82K4NtUiOd/Ymtdg9ab9F99cteRIirrimDNSLCNQDNSNUaBqvRpsXM7DpiAV9ij24uzyGce0oNYL9RAx+Lwdju/m5pOdGCzFAaduuGOY0o7ByT/pxpa6N9FeU88v1t4SooHKW83wJEYElry2//13pVHJPK/djNF8eM7B2tAbBfgIGv52BsbcFuqSHiErnUc144cIFDBs2rMT3Q4YMwYULV+/Rp9PpEBQU5PIp7VVgQAlINIoRseuUs1u8JMnY/Y8NzeLcn+zN4lXYdcq1otl50u5YPi5UQGSA4LLNfJOMQynOZZ6/Q48vxvjj86LPe0Wv+M4baMDEbkrDY7SW7AkmFmWlvA9KtCoBjaqLLvmQZBm7T9nRLM79/moWp3LJEwDsLLaf4kIu59u5zXyzjEPnnPluHKOCVgWcynA2zFa7jHM5MmKClcxeznfxrF/+2VsPiAJ0AhLCVY5PUqSopP2fK47ZudKDrVq1gEbVVS7rSJKM3adsjnUaxaihUcFlv/2Tbsf5HLnEdgVBQHSQCL1GwNeHLageLKBhde80OJX5eLtz+ThbKuAJsGNf/OtuX5Ryzse5OefL2HdX89lBK7rWVSHMwyDitSpRzqNKKecptrLLeYybcv6PtVwPIgDg2EUbRKHkq5rlVTLfqqJ8O3uf5pskJd81ysq3GrtOOteRJBm7T1rRrIbzQjTfJGHEh7nQqIB3hgRCd8X4rgseDMQXjwfj8/HK53/9lEkHPhoVhMFtb9zkAhVRB/wXVNT5fjkAeDpTxodDDAj1q0QBQACQrJAv/gExoX2xLwWICe0hnT/g+WbP7YMYWtvlOyGsFuTccx5vs7wC9CISIjWOT1I1DSKDVNh13DmIfL5RwqF/zWhW87/bW7NkPtWIDBKx669i+TRJOHS69Hxq1QIaxWmx6y9nT3tJkrH7hHMdo0Vpr0tcfwpCmddgx85ZlPrczU03Xb/KerwjAlXw14n49kAhdBrBZYxCbwjQiUiIUDk+SVEqRAYK2HXS2Qs13yTh0FmbS1tcXNntd9nXLTKcgUd3nNenvu3xqlUBDaOA3SnO7yQZ2HMWaFrNd+m6XgF6EQkRascnKVqNyEDRpczmmyQcOmNBswT3D3GUcq7Brr+cZUSSZOz+y4xmCSXLyGe/FqJrIz3CAq5eVzmON3s4E10zj+4Ou3Tpgu3btyMpKcnl+19++QUdO3b0SsKuNLydDs9+bkTjGBWSY1VYvtsCo1VG/+ZKxTFlvRFRQQIm91AauqFttBi2rBBLd5rRpY4aX/9hxZHzdrxYNIaZIAgY1laLRT+bUTNMRGyoiAVbzYgKFNCjvrJbYkJcb7r8tEojXKPYgLRd6qixfJcFb/9oxp1N1CgwA/O3mBDjpeDQ8HZaPPuFScl3jIjle6xKvoteUZzyhRFRgSImd9c58728EEt3WdCljgpf/2HDkfMSXuxTLN9tNFi0vSjfIQIW/GhxyXeATsADrTRY+KMF1YJExASLWLpLqbR7NVT+7q21VXh1k4wXvzVjyC0aSDKweIcFKhFoU7NiLi6VY6ZTjlm4quiYKfnvUd/ZgAxflo8eDTQY0kbZJ8Pb6/Ds54VoHKtWys4uM4wWoH9zpaEK1AsY0FyLud8ZEVw03uFL3xjRLF7lEkBZ8osJHepoIArApj+tWPyLGfMH+kHlxfcsKuvx/ukvG9ILZDSJUWaK/TtVwqubzWgRr0JcSMU8ZR3eVoNnvzSjcXURyTEqLP+16Jxvqi7aFyZEBQrOfdFag2ErjEX7Qo2vj1iVfXGn88I72yjjQo6E1DzlQuFy4DMiQHD0DgaA05kSfjst4f1BN352SUEQMKydHot+NBXVTSos2FJUzhsUK+cf5inlvChINby9Hs+uL0DjWBWSY9VYvsuklPMWzguytDwJ6fkSzhTl+8QlO/x1EqoHiwjxE3HgjA2HUmxoU0sNf50yMc7sb43o21SLYEPFPk0XBAHDbjVg0Taj8/zeXFiUb2cehi/JQY+GWgxppzwdHn6rHs+uy1fyHafG8p0mGC0y+rdUjnu+ScKIZXkwWmS8OjAQ+WbZMaN3mL8AlSigRrhrnZVV1MMsMVJVYqzQiubtOgC49nLvS94+3612GRM+M+HPixIW3a+HXZYd420GGwRoyzlxl7fYflsCTe/XIF08DPnC71C1ehjQ+MF++DMAgKb3a5DzL8H286vKCqIGQkTRtZdKAyEwGkJUA8BSCDn7dNE2l0I7eC1UbR+DdOxrCNWbQpX8AKw/PO+LLLolCAKGdQ3Cou9yUDNSg9hwNRZ8nYWoYDV6NHV2kRm+4CJ6NPXDkM5BAIACs4QzxcYLS8mw4WiKGcF+KsSEKWUlu8COC1k2pOYoQeLL46pFBKkQGXRjZ8gWBAHDOgVi0Q+5Sj7DVFjwTS6iglXo0cTZw2X426nokWzAkI5Kl/3hXQLx7OoMNI7XIrmGFst/yoPRIqF/G+UBRe1oDRIi1JixJgvP3B2MEH8VNh8uxM4TJiwaFQEAOHDKjEOnLWhTRwd/nYiD/5ox+4ts9G3lh2C/ynHeu6Pz80dkgnNMzIi4mohrkIyC7CxkXTjrw5RdnS+PNwB8tD0PzWvq4KcTsPO4Ca9uyMGkPsEIquDj7Wi/txa132EqLNhU1H43LNZ+f1DUfrcvar87GvDs2jzl+jxejeU7LrffSvt1NtOObw6ZcWsdLcL8BVzMkbD4JyN0agGd6ynb/emYBen5EprEqeGnE/D3JTte/bYALRLUiAv1fbD7oWbAc5uBRlHKzL8rDwJGmzJbMABM/QGICgAmFj0LstiBk5nK/60SkJoPHE0D/DRAQogPMuCGUs79sWhzHmpGqBEbrsKCb/MQFaRCj8bOa4/h76ajRxMDhnRQyvHwTgF49pMsNI7XILmGBst/LlCO9xWv/J5Ot+G3fyx4f2TJHs0/HTUhPU9Ck3iNcrwv2vDqV7loUVOLuLAbW78T/Zd5dLbcddddmDJlCvbt24e2bdsCAHbv3o21a9di1qxZ2LBhg8uy3tC7sQaZBTIWbjMjrehVxsVD/ByvSJ3PkSAUG9yyRQ01XhtgwBtbzZi/RQmAvPWAAXWjnQ3CyFu1MFpkTN9oQq5JRssaKiwe4leil0hZ2tZW/s6SHWYs2WGGXiOgWbwKHwzxczub8HXnu1FRvn8syne0iMUPFs+3DEFw9uBqEa/Ca/31eGObGfO3ykq+7zegblSxfLfXwmgBpn9VLN+D/aArNsju0z10UAnKjZfJKqNprArLhhoQXDRJRO0IFd59wIC3f7bggaWFEAWgQTVlO95+NbS4kR10MFplTN9YWJR2NRYP8Xc5Zmey7MgqdOa3d2Otsg+3GovKjgqLh/q7vF43tZcBogBM+FSZba1DkgbT73Ttgv7z3zYs2m6CxQbUr6bC24P8HeMCektlPd46NbB2vwVzvpdgsQPVggT0rK/BqA4V99pW70YaZBbKWPiTpdi+MDj3Re4V53y8Cq/1K9oX2yzKvrhP77Ivtp6w4bkNzieXk9Yr/x/XSYPHOzuDhesOWlEtSMCtib65gBzZsaicbyhWzocFuJbzTAlZhc6nnr2baJFZIGHhFhPS8iU0qK7C4mEBLuX8k71mvL3N2UNhyJJ8AMDL/fzQv4UOWjXwzWEL3tpmgsUmIy5UxEPtdHj41hsTDB3ZUa/UyV/kK/lO0GDx8KCy852sK8q3EWl5yqtHi4cHOvJ95LwdvxfNkNxzXrbL39v8VEiluEkoriLqgK3HbXhug/O4T1qn/H9cJy0e71I5el15+3y/lCdj6wklCHTPYqPL31o+VI82NSvHzYJ07GvYDGHQdJgI+EdATj0Ky9rhQNFkIUJQDCA7j7cQEAXd8K8dP6tbPwp160chndkNyyfKmH/yxUOwfjEW6k5PQ93+ccg5Z2Hb+j9If355Q/N2NSN7BMFoljD943TkGiW0TNRj8WPRLm+GnEm3Iivf2ePzj9NmPLTgkuPnOeuV0fXvaeOPOUMjAQBbDxfiuY8yHMtM+lB5tXrcHcF4/M4bPyHGyO7KJA3TP81U8llbh8WjI13rtXQbsoq9Ftm7hR8yC+xY+G0O0nLtaBCrxeLRkY7X5jQqAe+NjsDrG3MwdnE6Ci0yakSoMefBMHRuqFy7aNUCvjlQiLe+y4HFDsSFqfBQ50A83DUQlVlC45aY/NEmx8/3PacEwHeuX4Hlz47yVbKuma+ONwAcPm3Bwm9zUWiWUDtag1n3heLuW/xvTL47GZR8f16s/X442DXfGXZkFRbLd7IOmfkSFm4udLbfDwc5XgfWqoF9p6xYscOIXKMy4WOrmhp8PDbYMaGITgOs3WvCnK/tsNhkVAsW0bOxDqM6u3+N9Ea7oy6QaQTe2gOkFwD1I4H37nK+DnwhHxCK3S6mFQD3fuL8+cMDyueWWGBZ/xub9rKM7BqgHO/PspVyXkuLxY+GlzzeBc76u3dzg3K99n1eUTnXYPGo8BKvA6/7tRDVglW4tW7J6xOdRsDa3QWY86VNOd4hKvRsYsCo7r6fCOY/iRODVFmC7MHsFaJ4bQVGEATY7df2rqD88S3Xm4ybww0YXLVSUlWuG+8b5hrPh5vOf3SSnHLTVtExl66xjbjpmN3PcHfTq6Lnt/n8f3h6x3LQNa/j6yT4xs06m8lVjJmw3ddJ8IlFb1bMm02VntF49WVuQvbz6Vdf6Cakqhnj6yT4hNBnm6+TUCnIZ173dRJ8Tqgx2ddJ8AmPHoVLkg+naSUiIiIiIiIiIqLrUkW7axAREREREREREVUdHg+Ks3fvXmzbtg2pqaklegbOmzev3AkjIiIiIiIiIiIi7/AoCPjyyy9j2rRpqFevHqKjoyEUG9G0+P+JiIiIiIiIiKgS4cQgVZZHQcA333wTS5cuxfDhw72cHCIiIiIiIiIiIvI2j8K/oiji1ltv9XZaiIiIiIiIiIiIqAJ4FAScOHEi3n77bW+nhYiIiIiIiIiIiCqAR68DP/XUU7jzzjuRmJiIhg0bQqPRuPx+/fr1XkkcERERERERERERlZ9HQcAnnngC27ZtQ9euXREeHs7JQIiIiIiIiIiI/gs4MUiV5VEQcPny5Vi3bh3uvPNOb6eHiIiIiIiIiIiIvMyj8G9YWBgSExO9nRYiIiIiIiIiIiKqAB4FAWfOnIkZM2agsLDQ2+khIiIiIiIiIiIiL/PodeAFCxbg5MmTiI6ORs2aNUtMDLJ//36vJI6IiIiIiIiIiIjKz6Mg4D333OPlZBARERERERERUcXjxCBVlUdBwBkzZng7HURERERERERERFRBGP4lIiIiIiIiIiK6yXnUE9But2P+/PlYs2YNzpw5A4vF4vL7zMxMrySOiIiIiIiIiIiIys+jnoCzZs3CvHnzcP/99yMnJweTJk1C//79IYoiZs6c6eUkEhERERERERERUXl4FARctWoVFi9ejMmTJ0OtVmPQoEH44IMPMH36dOzevdvbaSQiIiIiIiIiIm8QBH6qKI+CgBcvXkSTJk0AAAEBAcjJyQEA9OnTB19//bX3UkdERERERERERETl5lEQMC4uDhcuXAAAJCYm4ocffgAA7N27FzqdznupIyIiIiIiIiIionLzKAjYr18/bNmyBQDw+OOP44UXXkCdOnUwbNgwPPLII15NIBEREREREREREZWPR7MDz5kzx/H/+++/HwkJCdi5cyfq1KmDvn37ei1xREREREREREREVH4e9QScPXs2li5d6vi5bdu2mDRpEtLS0jB37lyvJY6IiIiIiIiIiIjKz6Mg4HvvvYf69euX+L5Ro0ZYtGhRuRNFREREREREREQVQBD5qaI8nh24evXqJb6PjIx0TBhCRERERERERERElYNHQcD4+Hjs2LGjxPc7duxATExMuRNFRERERERERERE3uPRxCCjRo3Ck08+CavVim7dugEAtmzZgmeeeQaTJ0/2agKJiIiIiIiIiIiofDwKAj799NPIyMjAY489BovFAgDQ6/WYMmUKpk6d6tUEEhERERERERERUfl4FAQUBAFz587FCy+8gKNHj8JgMKBOnTrQ6XTeTh8REREREREREXmN4OsEkI94FAS8LCAgALfccou30kJEREREREREREQVoOrOi0xERERERERERFRFMAhIRERERERERER0kyvX68BepdH4OgV0I5nMvk6Bb6iqaNxdo/V1CnzDavV1CnzCdj7D10nwCXVshK+T4Bt1mvo6BT6hizjp6yT4xvlUX6fAN7SV55L5Rlr0ZkdfJ8EnxkzY7usk+MTEVlXzOrX+6qpZn9ve6urrJPhE1azNiZx4DhARERERERERVRUCJwapqqrm4x4iIiIiIiIiIqIqhEFAIiIiIiIiIiKimxyDgERERERERERERDc5BgGJiIiIiIiIiIhucpwYhIiIiIiIiIioqhDYH6yq4pEnIiIiIiIiIiK6yTEISEREREREREREdJNjEJCIiIiIiIiIiOgmxyAgERERERERERHRTY4TgxARERERERERVRmCrxNAPsKegERERERERERERDc5BgGJiIiIiIiIiIhucgwCEhERERERERER3eQYBCQiIiIiIiIiIrrJcWIQIiIiIiIiIqKqQuDEIFUVewISERERERERERHd5BgEJCIiIiIiIiIiuskxCEhERERERERERHSTYxCQiIiIiIiIiIjoJue1iUGys7MREhLirc0REREREREREZHXsT9YVeXRkZ87dy4+/fRTx8/33XcfwsPDERsbi99//91riSMiIiIiIiIiIqLy8ygIuGjRIsTHxwMANm3ahE2bNuHbb7/FHXfcgaefftqrCSQiIiIiIiIiIqLy8eh14IsXLzqCgF999RXuu+8+9OzZEzVr1kSbNm28mkAiIiIiIiIiIiIqH496AoaGhuLs2bMAgO+++w49evQAAMiyDLvd7r3UERERERERERERUbl51BOwf//+ePDBB1GnTh1kZGTgjjvuAAAcOHAASUlJXk0gERERERERERF5iSD4OgXkIx4FAefPn4+aNWvi7NmzeOWVVxAQEAAAuHDhAh577DGvJpCIiIiIiIiIiIjKx6MgoMViwVNPPVXi+4kTJ5Y7QURERERERERERORdHo0JGB0djUceeQS//PKLt9NDREREREREREREXuZREPCjjz5CZmYmunXrhrp162LOnDk4f/68t9NGREREREREREREXuBREPCee+7BF198gXPnzmHMmDFYvXo1EhIS0KdPH6xfvx42m83b6SQiIiIiIiIiovISRH6qqHLlPDIyEpMmTcKhQ4cwb948bN68Gffeey9iYmIwffp0FBYWeiudRERERERERERE5CGPJga57NKlS1i+fDmWLVuG06dP495778WIESOQkpKCuXPnYvfu3fjhhx+8lVa3ZFnGwi1GrN1rQq5JRosEDWbc5Y+aEaoy11u124Ql241Iz5dQv5oa0/r4ITleAwDILpSwcIsRO/624EK2hDB/Ed0bajGhhwGB+pJx06xCCfcszMGlXAm/TgtFkKHio8pVNd+r9lqwZJcV6fky6keLmNZLh+TY0vP83Z82vPmjGeeyZSSEiXiquxad6ziL/Q9HbfhkvxVHLtiRYwQ+H2VAg2qu2zuTKeGVzWbsO2uHxQZ0TFRjWi8tIgK8k9+F20x4+yeLy3e1wkV8+3iA2+XX7LPgy9+t+CvVDgBoVF2Fid31SI4r+9hfiz2nbJj7vQl/pUmoHiRgTCcd+jfXepzWilARZR8Apn+Rj10nrUjNleCnFdC8hhpP9fJH7cjy79erWfWrBUt2mJVyXU3EtDsMZR7P745Y8eZWM85lS0gIF/FUDx0613XmRZZlLNxmxtr9VmUfxaswo48eNcOd2xy7uhDHLtqRUSAj2CCgXW01JvfQITqoZLk+nSGh33v5UAnA3qlB3s18GcTkwRBbjQT8IiGnH4O07UXIlw65XzgsCap2T0KIbgQhKA72n/4P0oFlrtu7ZTSExJ4QwmoDNjPkC/th/+VVIOtUxWemDI4y/ZtZOV411Ndepn8xFZVpFab18UdynFK/ZRdKWLjViB1/W531eQONS32eVSjh6bUFOH7RhuxCGeH+Iro10GDSbX4I0AsVnm93ZFnGwrWnsHbLBeQW2NCiXjBmjKyLmtX9Sl3nvc9PY9OvafjnfCH0WhHN6wZj8uBE1I4puY4sy3h0ziFsP5iJt55qjB63RFZkdtySZRkLv83B2l35yDXKaFFLixkDw1AzSlPmequ252HJ1lyk59pRP1aLaQNCkZygc/z+TLoVr3yRjX3/mGGxyejYwIBpA0IREVTsvF+chmMpFmTk2xHsJ6JdXT0m3xWC6OByXQ5ek1W/WbFkj83ZfvfUIDmmjHruqA1v/mTFuRwZCWECnuqqReck5/ILf7bgmz/tuJgnQ6MCGlUT8WRnDZoWuyYYu9aMY5ckpZ7TA+1qqTC5qwbRgRV3vXIt9a87V2sHzFYZc38w4es/bLDaZNyapMaMO/Uu1yIvfWPC/rM2/JUqITFCxBdjS7bNsixj6U4L1uyz4nyOhFA/AQ/eosWYTroSy5aHUs5zsXZ38XIeipqR11LO85CeZ0f9GC2mDQhxKedpuXa8uiEbO4+bUGCWUStKjdG3BeH2ps7z/chZC17fmI3DZywQRQE9mxrw7D0h8NdV3l4fdVp1QM+Rk1CjUXOERMfgnccG4vfNG3ydLI+F3DYa4X0mQhUcDfOZw7i0fBJMJ3+76nqB7QYi9vEVyPttI87Nu8/xffXR7yO481CXZfN//wEpc+/2etrLQ5ZlLFjwIdau/Rq5uflo0aIxZs6ciJo140pdx263Y+HC5diwYRPS0zMRFRWBfv1ux2OPDYUgONvikydP49VX38fevb/DbrcjMTEBCxfOQkxM9I3I2nVbfQj4cD+QXgjUiwCe6wQkV3O/7N8ZwMI9wJ+pwPk8YEpHYFizG5pcoirFo9Zw/fr16Nu3L+Lj47F69Wo89thjOHfuHD766CN07doVQ4cOxZdffokff/zRy8kt6YPtJqzcZcLMuwOwZmwwDBpg5LJcmK1yqet8c8iMOd8UYFw3A9aPC0a9aiqMXJaHjHwJAJCaJyE1T8Izvfyx8YkQzB4QgO0nLHh+fYHb7U1bn4961So+UFBcVcz3N0esmLPJgnGdtFg/yg/1okWMXG1ERoHkdvn9Z+2YvN6Ee5tp8PkoP/Sop8L4NSacKAqeAYDRKqNlvApPdXd/8VtokTFitRECgGVDDFg93ACrXcbYT02Q5NL39fWqEyli++QAx2f1I6Xf8P76rw13NtZg+UP++GSEP6oFixixsgCXct3vh2uVkiVhzOpCtK6lxhdj/DGsrQ4vbDBh+9+ur/dfT1orQkWUfQBoFKPGy/0D8PWTIfhgeBBkACM+zIVd8t5xdpu2P6yY870J47rosH60P+pFqzDyowKXtBW3/4wNkz8z4t4WGnw+xh896qsx/hMjTlxylusPdliwco8FM/vosWakPwxaASNXFrrsoza1VJg/0IBvHw/Am/cZcCZTwoQ1xhJ/z2qXMXldIVrVqPggQXFC3d4QOz0H++63YFt9D5B2FKp+SwFDmPvlNQbIOWdh/+U1yAWp7peJbQ3p0CrYPhkI2/rhgKiBut+HgNpQcRm5Bh9sN2HlbjNm3u2PNWOClOO1PK/sMn3YjDnfFmJcVwPWPxaMetXUJevzXAnP9PLDxseDMbu/P7b/ZcXznzvrc1EAutfX4J0hgfjuyWDMHuCPXSetmLHBfZ1/I3yw4QxWfnsOM0fWxZr/awmDXoWRL/8Os8Ve6jp7j2bjwdtj8elLLbH0+Waw2SWM/L+DKDSVXGf5NynwTXjT6YMteVj5cx5m3heGNROjYdCKGLkotezjvb8Acz7Pwrjbg7H+6eqoF6PByHdTkZGn5LHQLGHEO2kQBGDZ+CisfjJaaasWp0EqVoe1SdJh/sMR+Pb5GLz5SCTOpNswYWl6hef5mz9tmLPFinEdNFj/iB71ogSM/MSMjAL3ed6fYsfkLyy4t5kan4/Qo0ddFcZ/ZsaJVGe9WDNcxAu3a7FhpB6rhuoRGyxgxCdmZBbbZpsEEfP7afHtGD3eHKDDmSwZE9Zb3P1Jr7mW+vdK19IOzP7ehG3HbXhzoAErHvZHap6Mxz8tWWcPaK5F70alB9r+71szPttvxZSeOnw7PgDvDPJDkzIepnrKUc4HhmHNxChlPyxKu0o5L8ScL7IxrlcQ1j9VDfViNRi5KM1RzgFgyqoMnEq14Z2REdjwTDXclmzAxGUZ+DNFOa6Xcux45N001IhQ49OJ0fhgTAT+vmjF1NWZXs+jN2n9/JFy7BA+fnGCr5NSboFt70XUkLlIX/9/+Pf5djCfOYT4ZzdAFVT2QxdNRA1EPTgbhUfdTzyZf/B7/DW2puNz/q2HKiL55bJ48SdYuXI9Zs6ciDVr3oHBoMeIEc/AbC693lm8+GN8/PGXmD79CXzzzXI89dSj+OADZTuXnTlzDg8++ARq147HypXzsWHDB3jssaHQ6bSlbteXvj0BvLIdeKw1sPYBJQg4egOQUcpLgkYbEB8ETGwPRNzY2wqiKsmjIODDDz+MmJgY7NixAwcPHsT48eMREhLiskxMTAyef/55b6SxVLIsY8UOI8Z0MaB7Qy3qVVNj7sAApOZJ2Hy09Mp22Q4TBrbSYUBLPZKi1Jh1tz/0GmDdPjMAoG60GgsfDES3BlrUCFehbaIGE2/zw7ZjFtjsrhcvH+9ReiM90uHG3URW1Xwv223FwOYaDGimQVKkiFl36qDXCFh30P0YlCt/taJDkgoj2muRGCliQlcdGlYXsWqv1bHM3ckajOukRbta7i+A95+141y2jNl361EvWoV60SrMuVuPP85L2H2q9JvS66USgchA0fEJ9S/91HxtgB8ebK1Fg+oq1I5U4aW79JBkYNc/zv1gscmY+70JnV7PQ/P/y8V9i/Ox51TZY3V+8psFcSEinr1dj8RIFYa00eL2hmos32X2OK3eVlFlHwDub63HLbU0iAtVoVGsGk/e5ocLORLOZZUvuHo1y3aZMbCFBgOaa5EUpcKsPnqlXB+wul1+5R4LOiSpMeJWHRIjVZjQTY+G1VVY9auSf1mWsWK3BWM66dC9vgb1qqkwt58BqXkyNh9zloHh7XRoFq9GbIiIFjXUeLSDFr+n2GG94lx/c6sZtSNU6NXoxgYBxRaPQPrjU8h/rgMy/4Z9y3TAZoTY6F63y8uXDkP6ZS7kE18Ddvdlwf7FCMh/rgcy/wbSj8H+wxQIQbEQohtXZFbKJMsyVuw0YUwXPbo3KCrT9/pfR5nWKeXmLj/39Xn9K+tzq6M+DzaIGNRGjyaxasSGqtAuUYNBbfTY969vxvWVZRkrvknBmP4J6H5LJOolBGDuuAZIzbJg897SA1UfPNcU/btUR514f9SvGYDZjzXA+XQzjvyT57Lc0X/z8OFXZ/F/Y+tXdFZKJcsyVvyUizE9g9G9iR/qxWoxd0g4UnPs2Hy49CFUlv2Yh4HtAzCgbQCSqmkw674w6LUi1u3OBwDsP2XGuUwbZg8OR70YLerFaDFncDj+OGvB7r+c9dzwrkFoVlOH2DA1WtTS4dEeQfj9tKXEee9ty361YWAzNQY0VSvt9x1a6NUC1v1eSvu914YOiSJGtNUgMULEhM5aNKwmYtU+5/J9G6nRvpYK8aEi6kSKeLaHFvlm4HixQOHw1ho0i1UhNlhEizgVHm2nxu/npArL77XWv1e6WjuQZ5Kxbr8VU27Xo21tNRrHqDD7bj0OnLXj4Fnndqf11mNway3iQt2Huk+m2fHJbxa8PciAbvU1iAsV0ThGhVsTvVu/y7KMFT/nYUzPIHRvYkC9GC3mDr5czksGLh374cc8DGwXgAFtisr5wFClnO9xPpg4eMqCIR0DkJygQ3yEGmN7BiPQIOLIWaW+/PGIEWoRmH5vKGpHa9Ckhg4zB4bih9+NOJ3mvl2tDI78/D2+fGMmDm767/b+uyys9xPI2fYhcn5aCcu5Y7i45HFIZiOCO5cRtBNEVB+3DOnr/gdrqvve+bLNAnvOJcdHKsiumAx4SJZlrFjxGcaOHYoePTqgfv1EvPLKVKSmpmPzZveBTQA4cOAIune/FV26tENcXDX06tUZHTq0wqFDxxzLzJ+/BJ06tcEzz4xBw4Z1UKNGLLp3vxXh4aE3ImvXbflB4N5GQL+GQFIYMKMroFcD6/90v3yTaOCpDkDvuoD2xvarIaqSPLp7v3DhAt577z3ccsstpS5jMBgwY8YMjxN2LVKyJKTly2if6HziGagXkRynxsEz7i+2LDYZR87b0D7J+eREFAW0S9Li4JnSLw7yTDICdALUKueF1d+pNryztRBz7w2AcAO7FlTFfFvsMo5ckNC+WLBOFAS0q6XCwRT3wbiDKXaX5QHg1toqHEy59qCOxQ4IcG2QdGqlB82+s94LAp7OlNDxtTz0eCMPT60rxPnsa0+j0QrYJCDY4DwY//vGhIMpdsy714Avxwbg9oYajPqoEP9mlJ7mg2ftaFfb9Ubg1iR1if1bnrSW140q+4UWGev3mREXKqJacMUFOZW0SWhfbL+LovJqbqnl+qwd7WtfUa6TnOdBSpas7KNi2wzUC0iOK/1cyS6UsfGwFc3jVdAUO9d3/2PDd0esmN5b73EePSJqIEQ1gnx2Z7EvZchndkKo3tx7f0ervConm7K9t83rVGaZPltWmba7rCOKAtolakpdBwDyTFKJ+ry4S7kSNh2x4JZaNzbge1lKqglp2Ra0b+K8qQn0UyM5KRAH/8q95u3kFSr7IDjAmQ+j2Y6nFvyJ6Y/UQWSId197vB4pGXak5UpoX9d5TgUaRCQn6HDwlNntOhabjCNnLS7riKKAdnX1OPivxbGMIABatfPY6jSC0lb9Y3K73ewCOzbuK0TzmjqX897bHO13TWddqrTfIg6ec99+HDwnoX3NK9tvEQfPua/DLHYZnx6wIVAH1I92X2dnG2VsPGJH8zixwvLrSf17Le3AkfN2WCW4LFM7UoWYYKHU7bqz7bgNcaEifjxhQ/c38tBtfh6mfWlEdqF3g6JllvN/yyjnKRa0r+s8P5Vy7rpOs1pafHOgENkFdkiSjK/3F8Jik9E6SefYjkYtQBSdx1ivUf6/75+K7QVKAFQa6Gs1R8EfW53fyTIK/9gKQ53Wpa4W0f852HPTkPPj8lKX8WvQEUnvnkat135H9CNvQgxw/2aAr6SkXEBaWibat2/p+C4wMABNmzbAgQNHSl2vefNG2L17P06dOgsAOHbsb+zb9wc6dVL2lyRJ+PHH3ahZMw4jRjyNdu36YeDAsWUGFn3JYlde620X7/xOFIC28cDvF32XLiJy8uhK38/P2U/XZDLBYnFtVIOCyh4zymw2w2x2vQjQWiXoNNd3s52Wp1w8hl8xNltEgIj0Ul6lyyqUYZeA8ADhinUEnEpzfxGUVSDh3R+NuO8W58WMxSZj8qf5ePoOf8SEqHA288YFQqpivrMKZdhlN+n3F3Aq3X0a0vNlhPtfmV8R6QXX3sulWawKBi3w2hYLJnbTQpaB17daYJeBtHzvXDQ3jVNh9j0G1AoXkZov4+0fzRjyYQE2PBaAAN3Vb1Re32RCVKDguDk4ny1h/QErtk4McIzvNuJWHX7524b1B6yY1MP9I7a0fNnt/s03AyarDL1GKHday6uiy/7q3Sa89n0BCi1ArQgRSx8Ocrmp9rayy7X7G7v0fLlk/v1FpBeVx7T8y/uo5Dav3EevbTJh1a8WGK1KOVz0oLNnb1ahhKlfGPFKf8ONHx/OEApBVAOFrr2/5MIMCGGJXvojAlSdp0E69xuQ8ZeXtnn9nMfLTZnOK6VuLrVMiziV7j6wnVUg4d1tJtx3S8kA2KRP87H1mAUmK9C1vgYv3ePvSVbKLS1buZYID3Z9vSkiWIv07Gu7eZckGS8v/xst6gWjbg3neGizl/+N5nWD0d0HYwAWl1b0WmN4oGs9HBGoQnpeKXVYgV053iXWEXEqVTnezWrqYNAKeG1DNib2CVbaqo3ZsEtA2hVDRby2IQurtufDaJHRtKYWix6t2H3iqOeubI/9BZzKuI72219w1HOXbfvLjslfmGG0ApEBApYO0iHUz3W917ZasGqfTannYkUsGlhxQeDrqX8vu5Z2IC1fGfcwyOC6TLibfVKWs1kSzmdL+P6IDXP7GWCXgDnfmzBhTSGWD/feeV96OReRnuu+bcsqkEop5yqcuuS8bnvjoQhMXJ6Bts+fh1oE9FoBCx+JQELRWINt6+gx94tsLNmai6GdAmG0yHj9qxwlXaX8bfIedWAEBJUathzXYTlsOanwi6nndh1DvfYI7jIc/z7XptTt5h/ahLy9X8Ka9i800bURed8sxE/5EqendwbkG3cPVpa0NOWV8yt754WHhyI9vfTX0R999EHk5xfijjsegkolwm6XMHHiCNx1120AgIyMbBQWGrF48cd48slH8NRTo7F9+68YP346VqyYh9atm1VYnjyRbYRSp13xWm+4H3AqyzdpotL4eoAU8hWPgoAFBQWYMmUK1qxZg4yMjBK/t9vLbmRnz56NWbNmuXw3fWAcZt4XX8oaio0HzZjxZb7j50XDKn6A+nyThNEr8pAYqcL47s4b5Nd/KERipAp3Nav4HgVVNd+VQZi/gDcG6DHrWzNW/mqFKAB3NlajYTURopfqzU51nL156gFoGqtCtzfy8N0RK+5tUfZYH+9vN+ObP6xYMdwfuqIn3SdS7bDLwB0L812WtdiBkKKboxb/5+xV0zdZg1l9r+217vKk1RM3uuz3baZF+yQN0vIkLP3FiCc/ycPHjwY79u3NZkR7LQY01+B8jhLQffZzExY9aIAgCJi+wYQ+TTS4paZveoVVNLHbTAgRdWBbM+iG/t2NB80uY+4tGhpY4X8z3yRj9Mo8JEapML5byXN9am8/jO9mwL/pdszbZMScbwsx466KDwRu3H4RMxafcPy86Nkm5d7mi0tP4K+zBVg9y9ljdOtv6dhzJAvr57Yq9/av18bfCjDjU+fN36LRFRNwCwtQ4Y2HIzBrTRZW/pyntFUt/NAwTlOirRrRLQgD2gbgfKYNb3+Xi2c/ysCiRyNdBqD/r2iTIOLzEXpkGWWsPWjDk59bsGa43iWAOKKtBgOaqnE+V8bb2614dqMZi+7TeSW/Gw9ZMWOj8/XWRYMr92BWkqxcC8zpp0etoomHXrrLgAHvF+CfdDtqX2UyotJs/K0AM9Y47+4XPRrhlfS68+a3OcgzSvjwsUiE+ovYfNiIicvS8dETUagXo0Wd6hrMHhyGuV9kY95XORAFYGinQEQEeu+6jbxH1Aeg+tgluPjBY7DnlbynvCxv11rH/81nj8B85jAS3zgKv4adUHjkxxuQ0pI2bNiEGTPmOX5+773ZHm3n229/xMaNm/H669OQlFQTR4/+jdmz30ZUVDj69esFSVKCnN27t8fw4QMBAA0aJGH//iP45JONlS4ISESVn0d3d8888wy2bduGd999F0OHDsXbb7+Nc+fO4b333sOcOXOuuv7UqVMxadIkl++0X/e86npdG2iRHB/i+NliU55+ZuRLiCo2o2V6voQG1d1nLdRPgEoEMq54cpqeLyPiiqew+WYZI5fnwV8n4K3BgS6vj+w5acWJS3Z8/4LSYF2eI6Ldy1kY3dmAJ3p470Kwqua7RPoFN+kvKJn+yyIChBKDjqfnS4jwv76rwA6Jamwar0ZWoQyVCATpBXSYV4D4kIp5TTTIIKBmuIjTV+lluWSHGYt/MWPpMH+XCVoKLYBKAD4b7Q/VFTc5fkVxus/HOHvIBBTFcyMDBLf7N0DnfJXG07R66kaX/UC9iEA9UDNChabxarR5KROb/rSgT9OKCXqXXa7dl6+IAKHEpCHpBZIjL5FF62Xky4gKLL6MXGLm61B/EaH+QK0IIDFCRJf5+TiYYkfzeDV2n7Jh63Fg6c6isQah3EA2mpWLF/vqMaACgr4OxizIkg3wc72RFPzCgYK0cm9e7DIdYq2usK19EMi/se+mKGXaWVZdynTglWXa/Q156WVaKlFuHPW5VsBbDwa4fQ1SGd9Teb0w2CBg8Ad5GNvV4JKeitC1VQSS6zgD+5aiCQMyciyICnWec+k5FjSoefVg6YtLT+DH/Rn4aGZzVAt39mDf/UcWzlwyovXDrq9OPfH6H2jZIAQrZ3jxFfMrdG1sQHKCczpEx/HOsyMq2Hl80/PsaBDrfjKHUH+VcrzzXB+wpudJiCjWa6pDfQM2TTcgK98OlSggyE9Eh2kpiA93rRtDA1QIDVChVpQGidU06DLjPA7+a0HzWhVcz13ZHhfIpbbHbttvN+29n1ZAQpiABCg9929/14jPfrdhdHvnvgz1ExDqJ6BWOJAYLqDLWyYcPCeheRkzsF+rrvXUSI51tqcW++Xz+er1b/H0Xa0diAwQYLUDuUbZpTdgRhnXQO5EBgpQi3AEAAEgMVL5GxdyZNT2MHanlHNnm2Ap6rhXspxLZZRzsZRybkdEUXt/Jt2GVdvzsXFKNdSprmynfqwW+/4xY/Uv+Zh1n/J6aN+W/ujb0h/peXYYtIIyuduPeYiPuDkfalUmtrx0yHYb1MFRLt+rg6Ngyy7Z3mqia0MbVRNxT61zfikox7veyjz8MznZ7RiB1tR/YctNgzY60WdBwG7dbkXTpg0dP19+My4jIwtRUeGO7zMyslC/flKp23nllUV49NFBuPPObgCAevVq4/z5S3jvvdXo168XQkODoVarkJhY02W9xMQa2LfvsBdz5B0hBuU+5MpJQDIKOekHUWXhUWu4ceNGrFixAl26dMHDDz+Mjh07IikpCQkJCVi1ahUGDx5c5vo6nQ46nevFpnwNrwIH6AQE6JwXE7IsIzJAwK5/rGgQo2Ql3yThUIoNg9q4H8NKqxbQKEaNXSet6NFQuWCRJBm7T1oxuK1znXyThBHL8qBVA+8MCSzRE2jBg4Ew2ZwXbIdTbHh+fQE+GhWEGmHeHdG0qubbJf0qAY2qi9j1rx096it5lmQZu0/ZMfgW9xeUzeJU2HXKjoeKvV2w85QdzeI8u7G9/IrR7lM2ZBTI6Fq3Yi4mC8wyzmZKuCu59Av7D34xY9F2Mz4YUnJWvwbVRNhlILNARqsE98ckIbzkPmgWr8JPf7m+Kr3zpA3NyrhRupa0lseNLPvuyHDe1FUEJW0idp2yoUcDjTNt/9gwuLX7IFuz+KJy3c753c6TdsdxigsVlH10yuYIIuWbZBxKsWNQq9IDd5cnEL188/bJSH/Yi8Uatx63YfEvZnw8wt/xmnmFkayQU49AiG8H+eTmoi8FCPHtIf2+slybFrtMh5h0G2yfDQFyU8qf1utUapk+aXUEspXjZcOg1u6DMkq5UWHXP1eU6X+sGNymeH0uY8TyXGhVgtv63B1nOajYiSIAIMCgRoDBWY/KsozIEC12Hc5yBP3yC2049HceBt0WW+p2ZFnG/z78C5t/TcOKGc0RF+Xa23HUPTVwb7fqLt/d9fRePPtQHXRrGY6KFKAXEaB3ni+yLCMySMSuEyY0iFOOXb5JwqHTZgzqEOB2G1q1gEbxWuw6YUKPZOUOSpJk7D5hwuCOJdcJDVDK1+4TJmTkS+jauPSe3kUdTCr0eDvbbwk9it4GlGQZu/+VMLil+3a0WazS3j/U2tm+7zwlodlVZrGV5LLz4ijfXnorVDmfneeV43y+jvr3WtqBRjEqaERg1ykbbm+oLPNPuh3nc+Qy2+grtYhXwyZZcCZTQo0wpVz+W/RKdkyw5+14qeX8Lzfl/NYyynmcFrv+Ml9Rzs2Ocm60KGm9skefKAiQ3Bz2y0HydbvzodMILmMUUgWxW2E6dQD+jboi/7eNyneCAL9GXZH1w6ISi1vOH8c/z7R0+S7yvpkQ9QG4tOIpWDPct9PqsFioAsLdBhZvlIAAPwQEOKNasiwjMjIMu3btR4MGStAvP78Av/9+FIMG3V3qdkwmMwTB9bpKpRIhF/W00Go1aNKkvmPMwMv+/TcFsbHR3sqO12hVQMMoYHcK0L1oBBdJBvacBQYl+zZtRKTwKIqRmZmJ2rVrA1DG/8vMVF516dChA8aOHeu91F2FIAgYdqsBi7YZUTNchdhQEQs2FyIqUESPBs6LreFLctCjoRZD2ikXwsNv1ePZdfloHKtCcpway3eaYLTI6N9SueG6HAgzWmS8OjAQ+WYZ+WalIg7zF6ASBdQId73oyip6Yp0YqUKQoWJvkKtqvoe31eDZL81oXF1EcowKy3+1wGiV0b+pUoynfKGMjTe5u5Kfoa01GLbCiKW7LOhSR42vj1hx5LyEF+903lhnG2VcyJGQWjT21uXxiSICBEePqnUHrUiMEBHmpwzA/X8/mPFQWw1qR3gnv3O/N6FrPTVigkWk5kl460czRFFAnybKhf6U9UZEBQmY3EO5eF38ixkLtpnx2gADYkNExzh5floB/joBtSJU6NtEgymfGzGlpzJzbGaBjF2nbKgXLaJLXfdB0wdaabHqVwte/cGEAc012H3Kju+O2Fxeb7paWitaRZX9s5l2fHPYgluTNAjzF3AxR8Lin43QqQV0rluBPd6gzNL77OdGNI5RITlWheW7i8p1c/fHf2gbLYYtK8TSnWalXP9hxZHzdrzYV+/cR221WPSzGTXDRGUfbTUjKlBwBNB/T7Hh8DkJLWuoEGQQcDZTwpvbzKgRKqB5vHKOJ0a6nut/nLdDFIC60Tdm2jZp/1Koer4C+dIfkC8egthiOKAxQPpT6S2g6vkK5IJLkHa8rqwgaoDwJOf//aOByAaApQDIOaN83XUmxPp9Yd8wVvn+ck9Dcx5gdz9YfUUTBAHD2uux6EeTs0xvMZYs00tzlTJdFLhWynQBGseoi5VpFKvPZYxYlgujFXj1QX+39flPxy1Iz5fRJE4FP62Av1PtePW7QrSooUZc6I2fnk8QBAzrHYdFn59Gzep+iI3SY8GnpxAVqkWPW5xdlIb/7wB63BKJIb3iAAAvLjmBr3ak4u2nG8PfoEJatnIsA/3U0GtViAzRuZ0MJCZCVyJgWNEEQcCwzkFY9EMOakaqERuuxoJvchAVrEKPJs66dvhbl9Aj2Q9DOinB0OFdAvHsqgw0rqFFcg0dlv+UB6NFQv82zoDKut35SKymQViAiIOnLPi/9Vl4qHMgakcrdcnv/5px+IwFLWvrEOQn4my6DW9+k40aEeoK6wXoyE9rNZ7daClqv0Us/9Wm1HPJRe33BqWOmtxVKfNDb1Fj2EdmLN1jRZdEFb7+04YjFyS8eIfy+0KLjEU7rehWR4XIAAFZhcDqfVZcypPRq0FRPXfOjsMXJLSMVyFID5zNkvHmz1alnoutmOuVa6l/AWD48gL0qK/BkDZKfq7WDgTqBQxoocHc700INiiBx5e+MaFZnArNivUsPp0hodAiIz1fhskGHL2gRDsTI0Vo1QLa11ahYXURz31pxHO99JBk4H9fm9C+tsqld6BX9kOnQCz6IRc1IzWIDVNhwTe5ReXcec4NfzsVPZINGNKxWDlfnYHG8Vok19AWK+fK8AS1ozVIiFBjxposPHN3MEL8Vdh8uBA7T5iwaJSzjvhoex6a19TBTydg53ETXt2Qg0l9ghHkV8EPsMpB5+ePyATnmLcRcTUR1yAZBdlZyLpwtow1K5/Mbxag+pjFMP6zD6aTvyH0jvEQ9X7I+WkFAKD62A9gyzyPtE+nQ7aaYUlxnTL28qy/l78XdP6IGPA88n79Avbsi9BE10bUg/8H66WTKDi06YbmrSyCIGDYsHvx7rsrkZAQi7i46njzzaWIiopAjx4dHMs99NAk3HZbRwwZ0g8A0LVrOyxa9BFiYqKQlFQLR4/+hQ8/XIsBA+5wrDNixP2YOPFF3HJLMtq0aY7t23/Ftm07sWLFGzc6m9fkoWbAc5uBRlHKzL8rDwJGmzJbMABM/QGICgAmtld+ttiBk0UjZ1glIDUfOJoG+GmAhBAfZIDoJudRELB27do4deoUatSogfr162PNmjVo3bo1Nm7ciJCQEC8nsWwjO+phtMiY/kU+ck0yWiZosHh4kEuPhzOZErKKzXzWO1mHzAIJC7cYkZanvEa4eHig47WLI+ft+L1ohsWe87Jd/t7mp0J8cnN0paqY796NNMgslLHwJwvS8mU0iBax+EGDI/3ncyWXJ2kt4lV4rZ8eb2wzY/42C2qGiXjrPj3qRjnzsfWEDc9tcN78T1qv/H9cJw0e76zcFP2bIWH+VgtyjDJiQgSM6aDF8DbeC3pdypUw+TMjso0ywvwEtKyhwqcj/RHmX5SvHNd8fbzXAqsdmLDG6LKdcZ21eLyrEiB4+R493v3ZjLk/mJCaKyPET0CzOBW6lNF7MS5UxKIH/TDnexNW7LGgWpCA/92lR8ck5zpXS+uNUBFlX6sWsO9fK1bsMCLXpEy80aqmGh+PDi4xYYO39W6sQWaBjIXbzEq5riZi8RA/Z7m+4vi3qKHGawMMeGOrGfO3KDeabz1gcAnOjbxVq+yjjSZlH9VQYfEQP8c+0msEbDpqxcIfzTBaZEQGCuiYpMbYgYYKnQjlesgnvoFkCIOq3QTALxJy+lHYvxgBFBaNGRQUAwHFun4EREEzeIPjR1WrkVC1GgkpZQ/snw1Rvmuq9FJXD1zl8rdsP0yB/Of6is1QGRxl+suCouOlxuKHAkuW6QJn18zeTXRKudliRFrRq8OLHypen9vwe9GsoT3n57j8vc2TgxEXqoJOI2DtbybM+dYOi01GtWARPRtqMaqT73rLjLyrBoxmO6a/fxy5hTa0rBeMxVObQldsivYzl0zIynNOgPLxpvMAgGGzDrps6+Wx9dG/i2sPwMpgZPdAGC0Spn+aiVyjhJa1dVg8Jsr1eGfYkFXg7K7Wu4U/MvMlLPwmB2m5djSI02LxmChEBDn3y7+pNsz/Khs5hRJiwtQY0zMIw7s430nVawVsOlSIhd/mwGiREBmkQscGBoztWbETIAFA74Zqpf3+2Yq0gqL2+36d41XW87nK7MaXtYhT4bW7tXjjJyvm/2hFzVABb92rQ90opXyrROBUuownDlmQZZQRYhDQpLqIVUN1qFP0eqteI2DTcTsWbrfCaFFeqe1YW4Wx/dQVmt+r1b/A5Taq2Pl8lXYAAKberocomDDh00JY7MpQJdPvdD1Xp20wYu9pZ7np954y/ujmCQGIC1VmzH13kB9e+taEIR8WwE8joGMdNab09P45r5Rz2bWcj4503Q/pNmQVG96idws/ZBbYsfDbonIeq8Xi0ZGOHn0alYD3Rkfg9Y05GLs4HYUWGTUi1JjzYBg6N3QGFw+ftmDht7koNEuoHa3BrPtCcfctvpnw6FolNG6JyR85A1r3PfcqAGDn+hVY/uwoXyXLI3m7P4MqKAKR906HKiQa5tOHcHbO3bDnKpOFaMLjnd2Qr4Vkh65GYwR3HAyVfwhsWRdQcHgz0ta8CNlWuWZ8HjXqARiNRkyf/jpyc/PRsmUTfPDBXOh0zod6Z8+eR1aWs12eNu0JvPnmUsya9WbRq8QRuP/+vhg3bphjmdtu64iZMyfi/fdX46WXFqJWrXgsWDALrVqVfyzdinBHXSDTCLy1B0gvAOpHAu/d5Xwd+EI+XOr8tALg3k+cP394QPncEgss639j016l/AfHAibvEOTLfY2vw/z586FSqfDEE09g8+bN6Nu3L2RZhtVqxbx58zBhwoTrToj8WfvrXof+w0y+6XXjc6rK+xS6QmluTE/BSsfqfqbWm53tUravk+AT6tiKGwy/UqvT1Ncp8I2LJ32dAt+4lH71ZW5G2io6nlxIsK9T4BNjJmz3dRJ8YmKrqnmdWn911azPbW919XUSfEI9/rivk1ApyOnlG2LnZiBEDPV1EnzCoyuaiRMnOv7fo0cPHDt2DPv27UNSUhKSk/myPxERERERERERUWXilceaCQkJSEhI8MamiIiIiIiIiIiIyMuuOQi4YMGCa97oE0884VFiiIiIiIiIiIiIyPuuOQg4f/58l5/T0tJQWFjomAgkOzsbfn5+iIqKYhCQiIiIiIiIiKgyEqrmGKAEXPORP3XqlOPzf//3f2jWrBmOHj2KzMxMZGZm4ujRo2jRogX+97//VWR6iYiIiIiIiIiI6Dp5FP594YUXsHDhQtSrV8/xXb169TB//nxMmzbNa4kjIiIiIiIiIiKi8vMoCHjhwgXYbLYS39vtdly6dKnciSIiIiIiIiIiIiLv8SgI2L17d4wePRr79+93fLdv3z6MHTsWPXr08FriiIiIiIiIiIiIqPw8CgIuXboU1apVQ6tWraDT6aDT6XDLLbcgOjoaH3zwgbfTSEREREREREREXiHwU0EyMzMxePBgBAUFISQkBCNGjEB+fn6Z63Tp0gWCILh8xowZ47LMmTNncOeddzom5H366afdvqF7Ndc8O3BxkZGR+Oabb/DXX3/h6NGjAID69eujbt26nmyOiIiIiIiIiIjoP23w4MG4cOECNm3aBKvViocffhiPPvooVq9eXeZ6o0aNwosvvuj42c/Pz/F/u92OO++8E9WqVcPOnTtx4cIFDBs2DBqNBi+//PJ1pc+jICAALFmyBPPnz8dff/0FAKhTpw6efPJJjBw50tNNEhERERERERERVSiz2Qyz2ezy3eU3XT119OhRfPfdd9i7dy9atWoFAFi4cCF69+6N1157DTExMaWu6+fnh2rVqrn93Q8//IA///wTmzdvRnR0NJo1a4b//e9/mDJlCmbOnAmtVnvNafTodeDp06djwoQJ6Nu3L9auXYu1a9eib9++mDhxIqZPn+7JJomIiIiIiIiIiCrc7NmzERwc7PKZPXt2uba5a9cuhISEOAKAANCjRw+Ioog9e/aUue6qVasQERGBxo0bY+rUqSgsLHTZbpMmTRAdHe347vbbb0dubi6OHDlyXWn0qCfgu+++i8WLF2PQoEGO7+666y4kJyfj8ccfd+nCSEREREREREREVFlMnToVkyZNcvmuPL0AAeDixYuIiopy+U6tViMsLAwXL14sdb0HH3wQCQkJiImJwaFDhzBlyhQcP34c69evd2y3eAAQgOPnsrbrjkdBQKvV6hLZvKxly5YeDUxIREREREREREQ3gFBxE2P8V1zPq7/PPvss5s6dW+Yyl+fL8MSjjz7q+H+TJk1QvXp1dO/eHSdPnkRiYqLH23XHoyDg0KFD8e6772LevHku37///vsYPHiwVxJGRERERERERETkS5MnT8bw4cPLXKZ27dqoVq0aUlNTXb632WzIzMwsdbw/d9q0aQMA+Pvvv5GYmIhq1arh119/dVnm0qVLAHBd2wXKOTHIDz/8gLZt2wIA9uzZgzNnzmDYsGEuXSqvDBQSERERERERERH9F0RGRiIyMvKqy7Vr1w7Z2dnYt28fWrZsCQDYunUrJElyBPauxcGDBwEA1atXd2z3//7v/5Camup43XjTpk0ICgpCw4YNrysvHgUB//jjD7Ro0QIAcPLkSQBAREQEIiIi8McffziWE9jFlIiIiIiIiIiIbnINGjRAr169MGrUKCxatAhWqxXjx4/HAw884JgZ+Ny5c+jevTtWrFiB1q1b4+TJk1i9ejV69+6N8PBwHDp0CBMnTkSnTp2QnJwMAOjZsycaNmyIoUOH4pVXXsHFixcxbdo0jBs37rrHMfQoCLht2zZPViMiIiIiIiIiIroprVq1CuPHj0f37t0hiiIGDBiABQsWOH5vtVpx/Phxx+y/Wq0WmzdvxhtvvIGCggLEx8djwIABmDZtmmMdlUqFr776CmPHjkW7du3g7++Phx56yKNJeT1+HZiIiIiIiIiIiP5bBEH0dRJuWmFhYVi9enWpv69ZsyZkWXb8HB8fj59++umq201ISMA333xT7vTxyBMREREREREREd3kGAQkIiIiIiIiIiK6yTEISEREREREREREdJNjEJCIiIiIiIiIiOgmx4lBiIiIiIiIiIiqDMHXCSAfYU9AIiIiIiIiIiKimxyDgERERERERERERDc5BgGJiIiIiIiIiIhucgwCEhERERERERER3eQ4MQgRERERERERUVUhcGKQqoo9AYmIiIiIiIiIiG5yDAISERERERERERHd5BgEJCIiIiIiIiIiuskxCEhERERERERERHSTqzwTg1itvk4BUcWzS75OgW+oZV+nwDeq6PFWh/j5Ogm+IVfRcp533tcp8A2T2dcp8A2RA4lXKUajr1PgExNbVc1+EvN/q5rXLYsytvk6CT4hcGKIKq5q1nPEI09ERERERERERHTTYxCQiIiIiIiIiIjoJscgIBERERERERER0U2OQUAiIiIiIiIiIqKbHIOAREREREREREREN7nKMzswERERERERERFVLM4OXWWxJyAREREREREREdFNjkFAIiIiIiIiIiKimxyDgERERERERERERDc5BgGJiIiIiIiIiIhucpwYhIiIiIiIiIioquDEIFUWewISERERERERERHd5DwKAu7fvx+HDx92/Pzll1/innvuwXPPPQeLxeK1xBEREREREREREVH5eRQEHD16NE6cOAEA+Oeff/DAAw/Az88Pa9euxTPPPOPVBBIREREREREREVH5eBQEPHHiBJo1awYAWLt2LTp16oTVq1dj2bJlWLdunTfTR0REREREREREROXk0cQgsixDkiQAwObNm9GnTx8AQHx8PNLT072XOiIiIiIiIiIi8iJOD1FVeXTkW7VqhZdeegkrV67ETz/9hDvvvBMAcOrUKURHR3s1gURERERERERERFQ+HgUB33jjDezfvx/jx4/H888/j6SkJADAZ599hvbt23s1gURERERERERERFQ+Hr0OnJyc7DI78GWvvvoqVCpVuRNFRERERERERERE3uNRELA0er3em5sjIiIiIiIiIiIiL/AoCCiKIgRBKPX3drvd4wQREREREREREVEFKSOeQzc3j4KAn3/+ucvPVqsVBw4cwPLlyzFr1iyvJIyIiIiIiIiIiIi8w6Mg4N13313iu3vvvReNGjXCp59+ihEjRpQ7YUREREREREREROQdHs0OXJq2bdtiy5Yt3twkERERERERERERlZPXgoBGoxELFixAbGystzZJREREREREREREXuDR68ChoaEuE4PIsoy8vDz4/T979x0fRdE/cPyz19N7KKEEEgw1dOkduyKC2KgKKoggRZ/HCqL+FHvhUVGKFFERARXEQrGgFGkBaaL0ThISUi/X9vfHhlyOXAKESw7l+3699gXZzG5mdmdnZ2dnZgMD+fjjj30WOSGEEEIIIYQQQgjhQ/JhkCtWuRoB33rrLY+fdTodMTExtGnThoiICF/ESwghhBBCCCGEEEII4SPlagQcPHiwr+MhhBBCCCGEEEIIIYSoIOVqBATIzMxkxowZ7Nq1C4BGjRpx3333ERYW5rPICSGEEEIIIYQQQgghLl25PgyyceNGEhISePPNNzl9+jSnT5/mjTfeICEhgc2bN/s6jkIIIYQQQgghhBBCiEtQrp6AY8eOpVevXkybNg2DQduFw+Fg2LBhjBkzhl9++cWnkRRCCCGEEEIIIYQQvlCu/mDiX6BcjYAbN270aAAEMBgM/Oc//6FVq1Y+i5wQQgghhBBCCCGEEOLSlasRMDQ0lEOHDlG/fn2P9YcPHyYkJMQnEfNm3u82ZvxWQFqOSv2qOp6+IYDkGvpSw3+3w87bqwo4mumidpSOR3ua6XKVsej3qqoy5ccCFmy2k2VVaVFTz8SbLcRHufc59ZcCftrjYPcJJ0Y9bHgi1ONvZOS5eGxhPn+edJGZrxIVpNA9ycC4HhaCLeX77PaFxKs8x6fArvLyD1a+2e7A7lDpkGhg4k0WooPdbwGOZbqY9I2V9fsdBJoUejczMq6HGYNe8fg78363cTTTRbUwHcM7mejdzFT0+79OOXnnxwJ2HHNy7IzKE9eZGdzOXK5jUSKNG2zMWGvX0lhFx9PXm0mOKyMP7HTw9k8FHM1UqR2p49EeJrrUc2f7H3Y5+GyznR3HnZzJh8X3B9Cgquf+Bs7JY8NBl8e6O1sYmHSTxSdpuhD+SHdqjotXV9hYs89Jrk2lTpSOBzuauK5BuacSvWiqqjJlZT4LNhZo10ItAxN7BREffZ5rYZ2VGb9aSctxUb+qnqdvDiK5hjve8zdYWbrVxs7jDnIL4PenwgkN8Hwbtj/Nyavf5bH5kAO7UyWpioHRPQNoW9d47p/zuco+30cyXfSckud132/1tXB9w8o55/M2OZix3kFaDtSPVXj6WiPJ1Ut/S/ndLidv/+Lg6BmV2pEKj3Y10CXRna4pq+0s2+niRLaKUQ+NquoY09lA0zhtn+sPOhn8id3rvhcMNtGkjL/tS6qqMmWV9Zx8Hnj+Mn+9lRm/Frjz+U2B5+TzApZuK5bPnwwrkc+n/pTPT3vshfc4hQ1PhVdEEi+YqqpM+SqdBaszycpz0SIxgIkDqhBfxVTqNhv25DHju9PsOGgl9YyT/42sTs/mnnWRx2ce58s1WR7rOjYKZPrYmhWSjrKoqsqU5bks+D2frHwXLeJNTLwthPjosq+zeWvymPFLLmnZLupXM/D0raEk13SXRwM/OM2GfZ75+c42AUzqo9Vbdh+z8+FPuWw+YCcj10VchJ672gYyqGOg7xPpLf4b7Z7l2nWm85drP9sKyzVFK9cStWNkd6q8/ZOdn/92cCRTJdis0L6OnnHdjVQJcefxEfOt7D7pIj1XJSxAoV28jvE9TB5hfJrGy7CeuvuEkw9/LWDzIScZeSpx4TruamVkUFvf1MtKo6oqU1bksWCDlax8lRa1jUzsHXz++/fafGb8kl9Yrhl4uleQRz6fsDiHtX/bOJXlItCs0LyWkUevD6RurJY3MnJdPDY/mz9POMnMcxEVrKN7AxPjrgsk2FL5vV7Cr3mQqJvHog+rQsGhPzg5exzWvRvPu11Iu37EjZpD9sYlHH3jjqL11R78kLAuAz3C5mz9gSMv3+rzuFeGeq06cu2wcdRq1JzwKtV576F+bF3xtb+jdUlUVWXK9J9Y8PUWsrKttEiuycTHbiS+ZtQFbf/hnF95Y+oqBt3RhifHXFe0/tCR07zyv+Vs2nYYm81Bp7aJPD3ueqIjgysqKaX6ZKvKzM2QlgdJ0fBUF0iuWvpz73d/qUxZB0ezoHY4jOsAXeLd4dPyVN74DX47BNkF0Ko6PNkV4sPdYSauUll3CE7lQqARmlWD8R2gbmT5nreFuJKV62545513MnToUObPn8/hw4c5fPgwn332GcOGDePuu+/2dRwBWLbdzuTvrYzsambRg0EkVdEz7ONc0nNcXsNvPuRg/Bf53N7CyOLhQfSsb+Dhz/LZc9JZFGb6bzbmrrfx7M0WPh8WRIBJYdjcPArsalEYm1Pl+kYG7mrt/QFEpyj0qG/kvbsD+W5UMC/1DmDtPicTl+aXO60XEq9zXcjxeel7Kz/+6eDtfgHMuTeIU9kqo+a74+l0qTz4SR52p8qnQ4OYfJuFxSl23vmxoCjMpxtsvLHCysNdzSx9KJhRXc08t8zKqj/dDx5WO9SM0DG+p4WYYN8VzMt22Jm83MbIziYW3R9IUhUdwz7JJz23lDxw2Mn4RVZub2Zk8f2B9EzS8/DnVvaccueBfLtKy5p6Hu1RdmW4X3MDq8cGFi2P9azYynNx/kr3f78qYH+6i/futPD1g4FcU9/A2IVWdh53lrqNr01fbWXuugKevTWIz4eHatfC7Oyyr4U/Cpj8bR4juwWw6KEwkqoaGDYr2+NasNqhUz0jD3YOKHU/w+dm43TB7PtCWDgijPrV9IyYm01qtvfj7iv+ON/VQhWP/L16bCCjupgINEGnxLIf2Hxl2U4nk1c6GNnRwKL7TFq659tIz/V+rjcfcTH+Kzu3N9Wz+D4TPevpeHihnT2p7uMUH6njmWsNfD3UxLwBJuLCFIbOt3E6T9tn8xo6Vo8yeyz9muqpEa7QuFrlVSqnry7Q8nmvQD5/MKQwn+ecJ5/bmPxtPiO7WVg0IpSkqnqGzc45J5+r583nNidc39jEXa0rr0wry/TvTjN3ZQbPDqjC50/WIsCsY9ibRyiwl37d5Re4qF/TzIT+Vcrcd6fGQax+PaFoef2B6r6O/gWZ/nMec3/L49nbQvj84UjtfM/ILPt8b7UyeWk2I3sEs2h0FEnVjAybkVGiDtTv6gBWPx1dtDx2o/vhcMdRB1HBOl65K4yl46IY3j2IN77L5uM13l8A+NKyHQ6tXOtkZNGwAO36/tRa+vV92Mn4xQXc3szA4vsD6Jlk4OHPC9hzSkuv1Q47Tzh5qJOJhcMCmHK7mf3pLh76vMBjP23i9bzZx8y3IwJ4u6+ZQ5kqjyws8PYnLz2Nl2k9dccxJ1FBOl7pE8DSh4IZ3snEGysK+Hi9zbcH4BzTf8ln7horz/YO5vOHwrW4zzxTdj7fVsDkb3IZ2SOQRQ+Hk1RNz7CZWR7HsFGcgRdvD+GbcRFMvzcMFRg6MwunS9uvTgc9Gpp4b1AI342P4KXbg1m7187EL3MrNL3ehLS9ndgBL5O26P848FQ7Cg5to+bjX6MPjSlzO2N0LWLveYm8Xb96/X1Oyvf8NSK+aDn2v8EVEf1KYQoM4sjubXz63CP+jorPTP94DXMX/M6zj93E59OHEmAxMmzsPAoKHOfd9o+dR5n/1WaSEj3vZ3n5NoaOmYeiKMyaMpBPPrgXu93JiMc+w+Uq/ZqqCN/uUXl5NTzUBr64C+pHwwNfQXqe93hsOa7y2HfQpyEsvBt61IVRS+GvdC28qqqMWgqHz8D/btbCVAuFoYshr1h50SgW/u8aWDoQpvXW1g37kqJrXwhx4crVCPjaa6/Rp08fBg0aRHx8PPHx8QwZMoTbb7+dl19+2ddxBGDW2gL6tTDSt7mJxFg9k262YDEqLNzivRfH3PU2OiYaGNrBTEKMnke6W2hYTc+837VKj6qqzFlnY3hnMz3qG0mqqufl2wI4la2yYre7kB7dzcKQdmauivV+qMICFO5ubaJJnJ64cB3t6hq4u7WRTYfK11ByofG62OOTbVVZuNnOf6+z0LaugcbV9bx0q4Uth52kHNb2+9teB3tTXbzSJ4AG1fR0rmfkkW5mPtlgw+bQCtivttq5s5WJGxsbqRmp46YmRu5oaWL6r+7KZJM4Pf+51sJNTYwYfdh+MGudnX7NjfRtZiQxRsekm8xaGlO8H5e5v9vpmKhnaHsTCTE6HulmpmE1HfM2uPPMrclGRnY20a5O2RENMCrEBOuKlmBz5TUQ+CvdKYedDGhtJDlOT80IHSM6mQixwI4TFdsIdpaqqsxZY2V4Vws9GphIqmrg5duDOJXtYsWu0h9eZv1mpV8rM31bmrVroVcgFiMs3OR+8Bvc3sIDXQJoWtN7z5uMXBcH013c39lCUlUD8dF6xl0bSL4d/jpZsY2g/jjfep1n/o4J1rFit4MbGhoIMlVOXp/1u4N+TfX0TTaQGK1j0vUGLAZYuM378Z670UHHujqGtjWQEK3jkS5GGlZVmLfJHf6WRnra19Hyb70YHY/3MJBTAH8WNiSY9Aoxwe4lPABW/uWkTxM9ilI56VZVlTlrrQzvUiyf9z2bz73f3wBmrSnM5y0K8/kthfl8s/vaGNzewgOdLTStWfp1PrpHAEPaW7iqSuU09pZFVVXmrMhg+M1R9GgeQlJNCy/fV5VTmQ5WbMkpdbvOTYIZc1sM17QoeySCyaAQE2YoWsKCKj/Nqqoy59c8hncPokcjC0nVjLx8Ryinspys2FF649Ss1bn0uzqAvq0DSKxiYNJtIVq5sMHzhWOAUSEmRF+0FO/51Ld1AE/1CuXquiZqRhno1SKAPq0CWL7dWmHpLYr/ejv9mhvc5dqNpsJyrZQ63AY7HRP0DG1n0q7vriatXNuohQ+xKMzsH8ANDQ3UjdLRrIaeZ643seO4i2Nn3PeoIW2MNKuh1c9a1NTzQHsjW4+4sDt9/9B4udZT+7Yw8dQNFq6ON1AzUkevpib6NDeyvIzy5VKpqsqc3/IZ3i2AHg3NJFUz8PIdwVq5trOM+/fqfPq1ttC3lUXL572DsZgUFm5059E7r7bQuo6RGhF6GsUZGHNNIMfPuDiaoZ33sAAdd7cNoEkNI3ERetolmri7jYVNByouvaWJvHE0Z378iDM/z8V2dDcnZozCVZBPWJcyGu0UHdVGziJt4fPYT+33GkR12HCeOVm0uHIzKyYBlWDHL9/z1VvPkrL8n9377yxVVZnz+XqGD+lEj85JJCVW4eUJvTmVls2KX3aXuW1uno1HJy3m+cdvJjTEc7TR5m2HOXoik5eevpWkhCokJVRh8jO3sn33MdZt8p5PKsqsLdCvMfRpqJAYpTCxO1gMsGin9/BzU6BjbRjaUiEhUmF0O4WGMTBvq/b7g5mw9QRM6AZNqijUiVCY2A0KHLDsT/d+7mis0CpOIS5UoWGswuh2cCJH610ohLg45WoENJlMvP3222RkZJCSkkJKSgqnT5/mzTffxGz2fW8Cm0NlxzEX7eu6H9h1OoV2dQ2kHPH+gJhy2En7up4V/A6J+qLwRzJUUnNUj32GWBSSa+hL3eeFOJnlYvkuB61rl2/4XHnidSHHZ8cxJ3YXHmHqxuipHqYUhUk57OSqWJ3H8OCOidpD89+FvWtsThXzOUmzGOCPo84KqVSfZXOq7Djuon2xRgydotCuTunHJeWI0yM8QIe6elKOXHwj1pLtdtq+lsMtU/N4fWUB+WW8yfYlf6a7WU09y3Y6yMxXcakq32y3Y3PA1bUr58H5SIZLuxYS3MOAQiw6kmsYihquz6VdC06PbXQ6hXYJxlK38SY8UKFOtI6vttjIs6k4nCrzN1iJClJoVMbwtUvl73x+1vbjTnaddNG3WcUPfYbCdJ9QaV/HXfboFG3oXspR7+lIOeqifbznLaxDndLD25wq81OchJihfikPy6v+cpGZD32SK69xyJ3Pzy3zLyCfn1vmX2Q+v9wcSbOTesZJ+wbu4akhgXqS61pI2Vv+3vVn/f5nHu3H/s31T+3j2bknyMipvF7NZx057SQ120X7eu5eWyEBOpJrGkk55L1xxOZQ2XHU4bGNTqfQLtFEyiHPho0lKfm0nXSKW95I4/Vvs8m3lX2vyraqhAVU7BDJUsu1eH3p1/cR10WXa9lWUIDQUqZiycxXWbLdQfMaOox63zby/5PqqaAdq7CAinvRcSTDRWq2SvvEYvncoiO5pqFEnj1LO4YO2id6uX8f8l6u5dlUFm2yUiNCR9Uw7/n4ZJaT5TsKaF2ncu5nRfRGLHWak7t9lXudqpK3fRUB9a4udbPoPk/izErlzE+zSw0T2KATie8fpM5rW6ly39vogiN9GXNxCY4cyyQ1PYf2reoWrQsJtpDcMI6U7UfK3Pa515fRtX092reuW+J3NrsDRQFTsd4VZpMBnU5h09ZDvkvAedicKjtPQdtiM2noFIV2NSHluPdtUo5Du3Nm3uhQW2v40/ap/WsuVhzqFAWTHjaXss88u8rinVAjFKpW3Exk/36KIssV6pImegoMDKRJkyYXvV1BQQEFBZ5vvE12F2aj9xt4Rp6KU4Woc4aWRgcp7E/zXhFKy1GJCtadE15HWo5WIU4tHFrgbZ9ppQzdKMu4L/JYtduB1QHdrjLwQq/yzRdXnnhdyPFJzdHmxAo9p9IXFaQUOyZqiX1EBWk/a39bT8cEA19sttOjvpFG1XRsP+bii8127C4tHrEhFXMxlZ1G78clLUctin9R+GAdabkX95B8c2Mj1cMUYoMV9pxy8dpKGwfSXUy5o/Qhdr7iz3S/1dfC2IVW2r6Wi0EHFiNM6WehdmTlzKnjvhbOuY6DdaRle3+wzchTcbq8HK9gHfvTLrwXgKIofHRvKCPnZdPy+Qx0CkQGKUwbHFKhD8z+PN/FLdxiJyFaoUUZPch8KSMPLd2BXtKdXlq6KZnuYuXZWT/+5WT8V3by7RATDDPvMhER6L2cWrjVScc6OqqGVl6lILUwviXvV+cp813ero3S74n/BKlntLhHhXpWTaJDDaSdubR0dWocxLUtQoiLNnI41cabi9J44K0jfPZkLfS6Sjzf2WWVa6Wdb5f38x2iY3+qu+Hw5mYWqofriQ3VseeEg9eW5XAg1cmUQeFe97v5gI1vt1qZeq/33/tKUblWopwq6/r2Uq4FKaSVMi1CgUPltVU2bmqkL9FT/7WVNuZt1MqApnE6pt7p+/l8/wn11LM2H3Lw7Q47U++puLkgfZ/PPe/fn6zN57XvcsmzQZ0YPTOHhmEyeB6ncZ9msWqXDasdujUw8UKfyp03zRASjaI34DhzymO948wpAqsned0mIKk9YV2HcODJNqXuN2fbcrI3fIU99QDGKnWJuWMSNf/7FQcndAG1ckZqiNKlntZ6rUdFBnmsj44MJu106T3av1m+nZ1/nuCLGcO8/r5ZoxoEWEy89t5Kxg7vjqqqvP7+SpxOldT00vfra5n5Wn0t+pziIyoQ9mV43yYtT/t9cdGBkFY4Qr9OBFQLgTfXwLPdVQKMMGeL1ssv9ZxR/J9uU3ntN8i3a9tN762N6hBCXJwLbgTs06cPs2bNIjQ0lD59+pQZdtGiRWX+/qWXXmLSpEke6yb0qcazt8ddaHQuO09cZ+HhLioH0l28sbKAyd9bmXjz+RuJlmyzM3GJu4fD1P6VM0F3eT3UxUxajspd03NRCyu8vZsZmf6bjUp8jqpUd7Zwvz1OqqInJlhhyMdWDp12UauSGsT84e2fbGRbVT4aYCEiQGHFn07GLrTy8eAAkipg6OCSlAImfu2+208d6L9Xe6qq8tySXKKCdcwbFojZqPDFxgJGfJzNghFhxFbQpPKXA6tdZel2ByM6lf4hhn+SNrV1LL7PREY+LEhxMuZLO58PNpVoYDiRpfLrfhdv9q7Y3iJLthYw8Wv3HGxTB1T+hN6XiyXrspg490TRz1NH16iwv3XT1e6PJSTVMJNUw8w1T+zn9z/zaNcgqIwtL82SLflMXJRd9HNFNrjd2cZdf0iqZiQmRM+QaRkcSndQK8qzurfnhIORczIZ2TOIjlddHvNBlpfdqTKmcJ6/Z28smZah7Yz0bWbg2BmVd3+x8fjXBUy901xpQ/4vJ3tOOhn5WT4ju5jpmOi7Dz4t2WJl4pfuhoipg8N8tm9vbmlupn09E6nZLmauzmfMJ1l8Ojwcs9F9Tp+4OZiHe7g4kObkje/zmPxNLhN7X77lrc4STLURMzgx/SGc2emlhsteu6Do/wWHd1Bw6A8S3tpFYMPO5O34qRJiKopb8v0fTHxladHPU1+7+Lnxj588w4tvfc/MtwdgPne4VaHIiCDeeuF2Jr26jLkL1qPTKdzUszENk6qh+4c/gBn1Cu/cpPL0Cmj3IegVredgp9pw7iv/m5OgXS2tAfGjzTDuW5jXT8Vs+GcfAyEq2wXXAMLCwooqTGFhl3Zzf+KJJxg3bpzHOtOX3UoNHxGooFcg/ZweHmm5qsfQ1eKig5USkzGn5bqILnyjGlO4XXqOSmxI8TBqia+kXoiYEB0xIdoQ27AAhf4f5TGii/m8jQXdkgwkx7krJbbCIbUXE68LOT4xwQp2J2Tlqx69AdNz1WLHROGPo577ODtp99n9WIwKL/YOYNItFtJzVGJCFD7fZCfIBJGl9K7xhbLT6P3vRgcrJSYdT8txER10afE8+zXDgxkV3wjor3QfOu1i3gY7Sx4MoF6slt76VfVsOuzkk412Jt3k+0bAbg1MJBebo+/sPJTpOS6P6ygtx0WDamVcCzovxyvHVWpZ4c26fQ5++tPO709FFH3lu1EvA2v22vlycwEPdKmYXqCXQz7/fpcDqx16J1fe0KmIQK3Sd+6k0mWnm5Lp9hI+0KRQO1KhNtAsTsd1Uwv4YquTB9t73v4WbXMSHgDd61XsNd2tvsnjC762wg6bJfL5+cp8HSXvcTml3xMvR92aBZNcJ77o56JrPstBbLj7GKVlOWhQ07cNVTVjTEQE6zl4yk67Bj7dtYduDc0eXzb1ON+h7vObluOiQXXvVbKIQJ33853tIjqk9LI4uZb2dw+mOT0aAf8+6eDeaRnccXUgI3pUfKNIUblWopy6yHItVyU6yDN/250qYxcVcOyMyqwBFq/z9UYEKkQEKtSJgoRoM13fySflqIvmZXy192L9E+qpf59ycu+cPO5oaWREF99eT90amkiuGVH0s7su6yI29Nz7d3nyuecxDLHoCLFAfLSepjUNtHkuneU7bNzczJ0urV6uo26sgbBAHf0/OMOI7oEe8alIjuw0VKcDQ1isx3pDWCyOzBMlwhur1MUUG0+NRxe6VypaXJPmZrNvfLLXOQLtpw7gyErFVCVBGgH9oFvHq0hu9GDRz7bCQj79dC6x0e4LN+10Dg3qVfW6jx27j5OekUufez8sWud0qmxMOci8hb+z7aen0Ot1dGyTwPIvRpGRmYderyM0xELHm1+nZvVGFZS6ksIDtPpa2jnfk0rPK9k78KzoQO33xaXlQXSx92+NYhUW3wPZBSp2p/ZMeed8lcaelw8hZoUQM8SHQ3JVlXYfwIq9cJP3zrVCiFJc8J3wo48+IiQkpOj/ZS3nYzabCQ0N9VhKGwoM2mTejarrWLvfPbzN5VJZt89Bs1Iqcc1q6lm733MIxpq9zqLwNSK0ieCL7zPHqrLtiLPUfV6osx8psl3AaLxgs0LtKF3Rkhiju+h4XcjxaVRdj1GHR5h9aU6OnVGLwjSrqWfPKZdHBey3vQ6CzZAY43l+jHqFqmE69DqFb7bb6XqVoULfRJn0Co2q6Vh7wH1OXarKuv2lH5dmNbzkgf1OmtW4tArg7pPa8Yn14ZePS+OvdJ+d81B3Tk8JneLO376mXQv6oiUxVut1uXavexiQdi04aFbKBz20a0HP2n3ubbRrwV7qNt6cTf+5HUWUCkw/XB75/IsUO92u0hN5iY3lF8OkV2hUVWHtAXfZ41JV1h100SzOezqaxelYe9DzYXHNgdLDu/frfkA9S1VVFv3h5NbGep/PFXaukvm8sMzfd26ZfyH5/Nwy/+Lyub8FW3TUrmIqWhKrm4gJ07N2l/tpISffybZ9Vpol+Lbh/cRpO5m5TmLDKvZ4BZt11I42FC2JVfTEhOhY+7d7GG+O1cW2w3aa1fLe+9ZkUGgUZ/DYxuVSWfe3jWa1Sm+s331MKweLN3r8dcLB4A8z6N3SwtjrK6dXVFG5tv+ccu2As/Tru4ZnOQgly7WzDYAHT7v4qL+l1GH+xV1M/exiXO711L9OORk8O4/eTY2M7eH74dBaPtcXLYmxemJCFNbuPTefO0rNs9oxNHjc810ulXV77TSrVfZ1qlKyXC+u6LxX4NzVJTjtWPdvIahRsU4OikJgo27k//V7ieC2Y3+y7z8t2f9Em6IlZ/M35O38mf1PtMGe7n0+OUNkHPrgKK8Ni6LiBQeZqV0jsmhJrBNDTFQwaze6G2xzcgvYtvMozRp77+3etlUdvp47nMWzHixaGtevzi3XNmHxrAfR6z3LyYjwQEJDLKzbuJ/0jFy6dbyqQtNYnEmv0DAW1h12r3OpKusOQ7Nq3rdpVs0zPMDaQ9DUS5toiFkhMlDhQKbKjlPQveT0iG7q2Wv/YlMhhChX7feFF16gf//+1KlTx9fxKdWQdmYeX5xP4+p6kuP0zF5nI9+u0qe5Vpn476J8YkMVxvfUKjcD25gYNCuPmWsK6FrPwDfb7ew45uS5W7TfK4rCoLYmpv5SQHykjrgIHe+sKiA2RKFnffdhOZbp4ky+yvEz2nwvu45rJU2tSB1BZoWf99hJy1VpUl1PoEnh71Qnr/5QQIuaempEXPxD+IXGa8jsXHrWNzKgjemCjk+IRaFvCyMvf28lLEAh2KzwwjIrzWroix4aOyQYSIjR8Z/F+Tx2jYXUHJW3VxVwT2tT0Vwr+9Oc/HHUSXINPVn52tfw/jrlYnJv9+scm0Nlb+GHROxOOJmtsuu4U+uRE1X+BrghbY08/lUBjavpSK6uZ/bvhWlsqsX/v19aiQ1RGN9DexM88Gojg+bkM3OtTcsDO+zsOObiuZvcb4oz81WOn3FxqnCOubPzE0UHa19LPXTaxdLtDjrX0xMeoLDnpIuXlhfQqpauQobEXi7prhuto3akwsRlVv7T00x4gMKKPx2s2edk6l2+f4DwRlEUBrW3MPUnK/FReu1aWJlPbIiOng3cD8tDZmbRs6GJAW21eA3pYOHxhbk0rm4guYaB2Wus5NugT0t3+lOzXaTluDh0Wrue95x0EmR2US1MR3igjuY1DYQGKDy+MIeR3QIwGxUWbCzgaIaLrkkV20POH+f7rIOnXWw86OLDuyvnHBc35GoDjy+107iqjuTqCrM3OMm3uz/S8d8lNi3dXbXjP7CVgUHzbMxc76Broo5vdjrZcVzluRu08Hk2lalrHHSvpzUmZ+SpfLLZyclslevre1676w66OJKp0q9Z5X8tVlEUBrUrzOeROuIi9MXyuTuvDfkom54NjO583t7C44tyaRynJznOwOy1hfm8hfvaKMrnhef73HwOZ+9xLo6fceF0qew6rjU41IrUE1SJX0GHwmPRM4Kp36QTX8VEXLSRd75MIzbcQM/m7garIa8dpmeLYAZ013oe5VpdHDrlbmw4kmpn1yErYUF6qkcZybW6eHdJGte2CCE6zMDhVBuvLkilVqyRjo0qdwoORVEY1DGQqatyiY/Wa+f7h1xiQ/X0bOS+Zod8mEHPxmYGtNfiN6RTEI9/fobGNYwk1zAy+9c8rVxopeWHQ+kOlm6x0rm+mfBAHXtO2HlpSQ6t6hhJqqbloz0nHAz58DQdrzIzpFMgqdla+adXFCIruAfpkDZGHv+6sFyL0zN7vb2wXCusw32l1XPGd9fy78DWRgbNtTJznZ2uiXq+2eHQyrXC4b52p8ojCwvYedzF1LvMOFW1aH7NsAAFk15h61Enfxxz0bKmjlCLwuEMlbd/tlErQqH5Jb4M9JrGy7SeuuekkyGz8+iYaGBIO1PRfH16HUQGVcx5VxSFQR0CmLoqX7t/R+p5Z3meVq41LHb/nn5Gu3+31xr5h3QK4PEF2TSOM5Bc08Ds36zk21T6tNSOyeHTTpZtK6BDPRORQQonzriY9nM+ZoNClyRtvz/vtpGW46JJDQOBZoW/Tzp59dtcWtQ2UCOicsv408veodrwaeTv24R170YibngYnSWQMz/PAaDaiOk4Th8jdf4EVHsBtiOen1c9+9Xfs+sVcxDRfZ8i+/cvcWaewFilLrH3/B/2k3vJ3ba8UtPmK+bAIGJqJxT9HF0jnhoNksnNzCDj+OEytrw8KYrCoDvaMHX2auJrRhJXPZx3PvyJ2OgQenauXxRuyKg59OxSnwG3X01wkJmrEjy7vAUEGAkPC/RYv3BpCgnx0USGB5Ky/Qj/99b3DL6zLXVrR1da+gCGNIcnlkPjKipNqsCcFMh3wG0Ntd8//oNKbBCM66DVIQY2g8EL4aPNKl3iYdke2H4KJvVw7/O7v1QiA7S5AfekwUu/QI+60KG2to/DZ1S+3aN9UCQiAE7mwPSNYDZA5/jKTP2/jQyjvlKVqxFwwYIFTJw4kTZt2jBgwADuuOMOoqMrtgC6sbGR07kqU34sIDVHpUFVHdMGBBYNszh2xoWiuCszLWoZeK1vAG+tKuDNlVoF6n93BXBVsYabYR1M5NtUJiyxkmVVaVlLz7QBgR5zirzzYwFfbnW/lbztA23OstmDA2lTx6A1DmyyMfk7KzYnVA3VcW0DA/d3LP9QiwuJ16HTLjLy3L1gznd8QJu3UKdYeWR+HjYndEwwMOEm98O+Xqcw9Z5AJi21ctf0XAJMCr2bGhndzZ0WlwofrbWxP82FQQ9t4g18OjTQo8HzVLZadJwAZq6xMXONjda19cy9t/xzL93YyMjpPJUpP9u0NFbRMe2eAHceyDonD9TU89ptFt76sYA3f7RpeeAOC1fFuvPAqj0Onvza/ZGacYu0/4/sbGRUFzNGPazZ79AaYmxQLUzh2vqGSp0vzT/pVvjgrgBeX1XAiPlW8mwqtSJ0TL7VTJd6ldfTaFgni3YtfJVbeC0YmDY4pOS1UGyy+BubmLVrYWU+qYVDh6cNDvG4Fj773cq7P1qLfh4wXZuz68U+QfRpYSYiSMe0wSG8tTyPwTOzcbhUEmP1vNs/mPqlDGXyFX+c77MWptipGqrQIaHyG8NubKjX0r3aTmouNIhVmHaHqWhY87Es1aNnZosaOl7rZeStXxy8+bOD+AiF//U1clVhr2W9Dvanq4z+w0ZGvjaEpUk1HfMGmKh3Ts/mL7Y6aR6nUPcSXlJcimGdzOTbVSZ8nefO54OCvZT57l4sNzYxcTrXxZSVVnc+HxTsmc83FHjm8xnanF0v3hZInxbaeX9nVT5fbnE3oN32nnYtzL4vmDaV/TVNYNj1keQXqEyYc4KsPBct6wUwbUwNj9ECh1JtZGS7X/1vP2Bl8Gvuh8XJn6cC0Lt9KJPvq4ZeB38eKeDLNVlk5zmJCTfQoVEQj9wajamMUQgVZViXQK1cW5hNltVFy3gT0+4LP+d8O8jIdR//G5tatPP9Qw6p2drQ4Wn3RRQNBzbqFdb8bWP2b3nk21Sqhem5tomZEd3d99zv/7ByOlfl6y1Wvt7izhfVI3SsejymQtN8YyNDYblmJzXXppVrd1uKhr6WqMPV1PNabzNv/WQrLNcU/neHmasKv+x9Mltl1R4tD/SeZvX4W7MHWGgTr8diVFi+28GUX1zk27QpTzol6BnR0VjiIxI+SeNlWk/9fqed03kqX2+z8/U2d7jqYQqrxlbc3LvDOgdocV+co8W9tpFp94Z55vN0p2ddNtnM6RwXU1bkafm8moFp94YWDQc2GWDTfjtzfssnK1/7sEqreCOfjggr+qCI2QgLNliZ/I0Tm0OlapiOaxubub+CpvEoS/a6L9CHRhNz+wT04VUoOLiNw5NvxZmlfSzEGFUTXBfxMQ+XE3OtxoR16o8+KBxHxnFy/1hB6ufPoTq8f138cle7cUvGf+xuwLzjyVcBWLNoDrMfv99f0bokwwa0J99qY8LLS8nKsdIyuRbT3ujvMd/foaMZZGTmlbGXkg4cSuPNqSs5k5VP9WrhDB/ckSF3tfV19M/rhqsUTuerTFmnzc1XPwY+uBWiC3tjH8/GY5745tUUXrlO5Z218NYaqB0OU26GelHuQKm58MpqbZhwTBDcWh+GF/uItlkPm47B3BQ4U6ANMW4ZB5/0K/lROSHE+Smqqparb/yOHTuYN28en332GUeOHOGaa66hf//+9O7dm8DAi3+zrn7aujzREP9UTvmC2RXF/O/4yMRFK/hnVsovmcPHY+3+KQIr/yHzslAt9vxh/o3SM/0dA//IrrwvUV5WDJX/cuSycIXev//8YrO/o+AXb268MuvnU9fO9HcU/ML12fP+joJf6Efu9ncULg+53/o7Bv4XdIO/Y+AX5X4F3qhRI1588UX27dvHjz/+SHx8PGPGjKFqVe+TngohhBBCCCGEEEIIIfzDJ+NggoKCCAgIwGQyYbfbz7+BEEIIIYQQQgghhBCi0pS7EXD//v383//9H40aNaJVq1Zs2bKFSZMmceKEfJ1KCCGEEEIIIYQQ4rKk6GS5QpVrhvu2bduyYcMGkpOTuffee7n77ruJi4vzddyEEEIIIYQQQgghhBA+UK5GwB49ejBz5kwaNmzo6/gIIYQQQgghhBBCCCF8rFyNgP/3f/8HgM1mY//+/SQkJGAwlGtXQgghhBBCCCGEEEKIClaugdD5+fkMHTqUwMBAGjVqxKFDhwAYNWoUkydP9mkEhRBCCCGEEEIIIYQQl6ZcjYCPP/44W7du5aeffsJisRSt79mzJ/Pnz/dZ5IQQQgghhBBCCCGEEJeuXGN4v/zyS+bPn0/btm1RFKVofaNGjdi7d6/PIieEEEIIIYQQQgghfKhYO464spSrJ2BqaiqxsbEl1ufm5no0CgohhBBCCCGEEEIIIfyvXI2ArVq14ptvvin6+WzD3/Tp02nXrp1vYiaEEEIIIYQQQgghhPCJcg0HfvHFF7nhhhvYuXMnDoeDt99+m507d7JmzRp+/vlnX8dRCCGEEEIIIYQQQghxCcrVE7Bjx45s3boVh8NBkyZN+OGHH4iNjWXt2rW0bNnS13EUQgghhBBCCCGEEEJcgovuCWi323nwwQd55plnmDZtWkXESQghhBBCCCGEEEJUCPmWw5XqonsCGo1GFi5cWBFxEUIIIYQQQgghhBBCVIByDQfu3bs3X375pY+jIoQQQgghhBBCCCGEqAjl+jBIvXr1eO655/jtt99o2bIlQUFBHr8fPXq0TyInhBBCCCGEEEIIIYS4dOVqBJwxYwbh4eFs2rSJTZs2efxOURRpBBRCCCGEEEIIIYQQ4jJSrkbA/fv3F/1fVVVAa/wTQgghhBBCCCGEEJcxpVwzw4l/gXKf+RkzZtC4cWMsFgsWi4XGjRszffp0X8ZNCCGEEEIIIYQQQgjhA+XqCThhwgTeeOMNRo0aRbt27QBYu3YtY8eO5dChQzz33HM+jaQQQgghhBBCCCGEEKL8ytUI+P777zNt2jTuvvvuonW9evUiOTmZUaNGSSOgEEIIIYQQQgghhBCXkXINB7bb7bRq1arE+pYtW+JwOC45UkIIIYQQQgghhBBCCN8pVyPgwIEDef/990us//DDD+nfv/8lR0oIIYQQQgghhBBCVARFlitUuYYDg/ZhkB9++IG2bdsCsH79eg4dOsSgQYMYN25cUbg33njj0mMphBBCCCGEEEIIIYQot3I1Am7fvp0WLVoAsHfvXgCio6OJjo5m+/btReEU5cptXRVCCCGEEEIIIYQQ4nJRrkbAH3/80dfxEEIIIYQQQgghhBBCVJByzQkohBBCCCGEEEIIIYT45yj3nIBCCCGEEEIIIYQQ4p9Gpm67UklPQCGEEEIIIYQQQggh/uWkEVAIIYQQQgghhBBCiH85aQQUQgghhBBCCCGEEOJfThoBhRBCCCGEEEIIIYT4l7t8PgziUv0dA1GZzCZ/x0BUptw8f8fAP4IC/R0D/4iN8ncM/ONUur9j4BeZS3/3dxT8IqxOhL+j4BdKbKS/o+Af+QX+joFfOI+l+TsKflH/k73+joJfTE3/0d9R8Ivh7e7zdxT8YupbHfwdBeFPivQHu1LJmRdCCCGEEEIIIYQQ4l9OGgGFEEIIIYQQQgghhPiXk0ZAIYQQQgghhBBCCCH+5aQRUAghhBBCCCGEEEKIf7nL58MgQgghhBBCCCGEEKKCKf6OgPAT6QkohBBCCCGEEEIIIcS/3CU3AlqtVl/EQwghhBBCCCGEEEIIUUHK1Qjocrl4/vnniYuLIzg4mH379gHwzDPPMGPGDJ9GUAghhBBCCCGEEEIIcWnK1Qj4wgsvMGvWLF555RVMJlPR+saNGzN9+nSfRU4IIYQQQgghhBBCCHHpytUIOGfOHD788EP69++PXq8vWt+0aVN2797ts8gJIYQQQgghhBBCCF9SZLlClasR8OjRoyQmJpZY73K5sNvtlxwpIYQQQgghhBBCCCGE75SrEbBhw4asXr26xPovvviC5s2bX3KkhBBCCCGEEEIIIYQQvmMoz0YTJkxg8ODBHD16FJfLxaJFi/jzzz+ZM2cOS5cu9XUchRBCCCGEEEIIIYQQl6BcPQFvvfVWlixZwooVKwgKCmLChAns2rWLJUuWcM011/g6jkIIIYQQQgghhBBCiEtQrp6AAJ06dWL58uW+jIsQQgghhBBCCCGEqFDl6g8m/gXK3Qh4Vk5ODi6Xy2NdaGjope5WCCGEEEIIIYQQQgjhI+Vq/t2/fz833XQTQUFBhIWFERERQUREBOHh4URERPg6jkIIIYQQQgghhBBCiEtQrp6AAwYMQFVVZs6cSZUqVVAUxdfxEkIIIYQQQgghhBBC+Ei5GgG3bt3Kpk2bSEpK8nV8hBBCCCGEEEIIIYQQPlau4cCtW7fm8OHDvo6LEEIIIYQQQgghhBCiApSrJ+D06dMZPnw4R48epXHjxhiNRo/fJycn+yRyQgghhBBCCCGEEMKHZEq3K1a5GgFTU1PZu3cv9957b9E6RVFQVRVFUXA6nT6LoBBCCCGEEEIIIYQQ4tKUqxHwvvvuo3nz5nz66afyYRAhhBBCCCGEEEIIIS5z5WoEPHjwIF9//TWJiYm+jo8QQgghhBBCCCGEEMLHyvVhkO7du7N161Zfx0UIIYQQQgghhBBCCFEBytUT8JZbbmHs2LH88ccfNGnSpMSHQXr16uWTyAkhhBBCCCGEEEIIX5Ip3a5U5WoEHD58OADPPfdcid/Jh0GEEEIIIYQQQgghhLi8lKsR0OVy+ToeQgghhBBCCCGEEEKIClKuOQGFEEIIIYQQQgghhBD/HOVuBPz555+55ZZbSExMJDExkV69erF69Wpfxk0IIYQQQgghhBBCCOED5WoE/Pjjj+nZsyeBgYGMHj2a0aNHExAQQI8ePfjkk098HUchhBBCCCGEEEII4ROKLFeocs0J+H//93+88sorjB07tmjd6NGjeeONN3j++ee55557fBZBIYQQQgghhBBCCCHEpSlXI+C+ffu45ZZbSqzv1asXTz755CVHqizzNtiYscZGWo5K/So6nr7BQnKcvtTw3+208/aPNo5muqgdpePRHma61HMnW1VVpvxkY8EWO1lWlRY19Uy80UJ8lNZJcv0BB4Pn5Hvd94KhgTQ5528fPO3itg9z0Suw4b8hPkixd74+Dj/ssvPZJjs7jjs5kw+LHwikQVXP/c3fZGPpdgc7jzvJtcHv/wkm1FK5LeiqqjJlZT4LNhZo56uWgYm9goiPLj3tAPPWWZnxq5W0HBf1q+p5+uYgkmu40z9/g5WlW23sPO4gtwB+fyqc0AB3R9n1++wMnpntdd8LhofSpEa5LqUL5q90A+w45uD17/P446gTnQLXNjLx+A2BBJkr/tzP2+RgxnoHaTlQP1bh6WuNJFcvvQPzd7ucvP2Lg6NnVGpHKjza1UCXRPcxmrLazrKdLk5kqxj10KiqjjGdDTSNc++z+3tWjp3x3O+4rgYeaFex57i4ijjfmXkupqzK57e/7RzPdBEZpKNHAyOP9AwgxOJO/wtLc9l8yMFfJ50kxOj58uGwCk3rxVJVlSnLzrBgTQ5Z+S5a1DEz8c5I4mONpW6z4W8rM1ZmseOQjdQsJ/8bFkPPpoGVGOuyzdtgY8Zau7s8v958nvLcwds/FXA0U6V2pI5He5jOKc8dfLa5WHl+f0CJ8nzgnDw2HPT8yNedLQxMusni28RdBHO7ezF3fghdSAzO4zvJ++opnEe2eA1ruro/phb90FepD4Dz6Dbyv3vJI3zEyye8bpv3zXMU/PKe7xNwgealqMzcpJKWC/Vj4KluCslVSy9Pv9uj8s4alaNZUDscxndS6FLHHT7XpvLGryor90JmPtQIgwHNFO5q6g4zcYWLtYfgVA4EmqB5NW0/dSMr7x6uqipTVuSxYIOVrHyVFrWNTOwdfP5ybW0+M37JLyzXDDzdK4jkmu7rfcLiHNb+beNUlotAs0LzWkYevT6QurHaNZGR6+Kx+dn8ecJJZp6LqGAd3RuYGHddIMGWip8a+0qtr12IT7bBR5shLQ+SouHJzpBc1XvYv9NhynrYeQqOZcN/O8GgZpUa3XJRVZV33vmIBQu+ISsrhxYtGvPss2OJj69R6jZOp5MpU2bz9dfLSUs7TWxsNLfddh0PPTQQRXGfx717D/Lqqx+yYcNWnE4nCQm1mTJlEtWrV6mMpJ2XqqpMmf4TC77eQla2lRbJNZn42I3E14y6oO0/nPMrb0xdxaA72vDkmOuK1h86cppX/recTdsOY7M56NQ2kafHXU90ZHBFJcXn6rXqyLXDxlGrUXPCq1TnvYf6sXXF1/6O1gVTVZUp32WzYG0uWVYXLeLNTOwXTnxM2XXleb/mMGNVDmnZTupXN/J0n3CSa5sAOHLaQc/nT3rd7q3BkVzfLACAPw7ZeH1pFjsO21AUaFLLxGO3hFE/rvR6oBDCU7lqPzVr1mTlypUl1q9YsYKaNWtecqRKs2yHnck/FDCyi5lFDwSSVFXPsHl5pOd6/1rx5sNOxi+0cntzI4sfCKRnkoGH5+ez55SzKMz0NTbm/m7j2ZvMfD40kACjwrB5eRQ4VACa19SzelyQx9KvuZEa4QqNz2mMsDtVxi/Kp1Wtsiu0l6oijkO+HVrW1PNoD3Opf9dqh04Jeh7saPJ5mi7U9NVW5q4r4Nlbg/h8eCgBJoVhs7MpsKulbrPsjwImf5vHyG4BLHoojKSqBobNyiY9x328rHboVM/Ig50DvO6jeS0Dq/8b7rH0a2mmRoSOxmVU5n3FX+k+meXivo+yqRWlZ/6DoUwfHMLfp5w8sSjH52ksEf+dTiavdDCyo4FF95lIqqJj2Hwb6bne07z5iIvxX9m5vamexfeZ6FlPx8ML7exJdac3PlLHM9ca+HqoiXkDTMSFKQydb+N0nuc+R3cysHqUuWgZ0LLiz3FxFXG+T2W7OJXl4j/XB7JkVBgv9Qli9V92nlqcW2JffVuYubGJ/67zskxfkcXcn7N49s5IPh9flQCzwrD3TpV5bPILVOrHGZlwR2QlxvTCLNthZ/JyGyM7m1h0f6CWzz/JL7s8X2Tl9mZGFt8fSM8kPQ9/bj2nPFfPW54D9GtuYPXYwKLlsZ5lh69IxuRbCbj5WawrXyfrnWtxHt9B8NBPUYKivYY31G2PPeVLcj7sS/Z7N+M6c4zgYZ+hhLpbEDKfb+Kx5C4Yg+pyYd++tLKSVcKyP1Ve/kVlZFuFhf0VkqLh/kUq6Xne8++WYyqPLlPp21hhUX+FHokw6muVPWnu8C//rPLrAXjleoVvBisMaq7wwo8qq/a6wzSKVfi/a7XfT7tNQQWGLVJxukq/bnxt+i/5zF1j5dnewXz+ULhWrs08U3a5tq2Ayd/kMrJHIIseDiepmp5hM7M87mON4gy8eHsI34yLYPq9YajA0JlZRWnT6aBHQxPvDQrhu/ERvHR7MGv32pn4Zcmyz9eu5Pra+Xy7B15ZDQ9dDQvu0hoBH/wa0vO8h893QM1QGNseoi+fdzjnNW3aZ8ydu4hnnx3L55+/R0CAhaFD/0NBga2MbT7l00+/YsKE0SxbNptHH32A6dO1/Zx16NBR7rlnNHXr1mTu3Df5+uvpPPTQQMzmy+ecT/94DXMX/M6zj93E59OHEmAxMmzsPAoKHOfd9o+dR5n/1WaSEj0bNPPybQwdMw9FUZg1ZSCffHAvdruTEY99hqsSy7NLZQoM4sjubXz63CP+jkq5TF+Vw9xfcni2Xzifj4nV6mJT08ouz7fkMfnLM4y8LoRF42NJqm5k2AdppGdr5Vu1cD2rJ1X1WEZdH0KgWaFTA628yy1wMeyDNKpF6Jk/NoZ5o2IIMisM+yANu/Ofc/6F8LdyNQKOHz+e0aNHM2LECObOncvcuXMZPnw4Y8aM4dFHH/V1HIvMWmujXwsjfZsZSYzRM+kmMxajwsItdq/h56630TFRz9D2JhJi9DzSzUzDajrmbdDCq6rKnPV2hncy0yPJSFIVPS/3tnAqW2XFbu0GZdIrxATripbwAIWVfzro08zo8TYO4O0fbdSN0nF9w4p9E+Hr4wBwa7KRkV3MtKtb+hucwW1NPNDRTNMaldsgcpaqqsxZY2V4Vws9GphIqmrg5duDOJXtYsWu0itTs36z0q+Vmb4tzSTG6pnUKxCLERZuKigKM7i9hQe6BNC0pvf0mwwKMSG6oiU8UGHlbht9WphL5ANf82e6f/rThkEHE24OpG6MniY1DDzbK5Afdtg5mO70uo2vzPrdQb+mevomG0iM1jHpegMWAyzc5v3vzt3ooGNdHUPbGkiI1vFIFyMNqyrM2+QOf0sjPe3r6KkZoaNejI7HexjIKYA/T3k+kAWZICZYKVoCTZXbW6YizvdVVQxMuSeE7vVN1IrS0zbByNhrAvlxtx1HsYrT0zcH0b+thRoRl9/H41VVZc5P2Qy/LoweyYEkxZl4eWA0p844WLGtlCdHoHOjAMbcHME1l1Hvv7NmrbPTr/nZ8lznLs9TvD8kzf3dXqw815Vennc20a5O2WV1gNHz/hZcCb17S2Pp9CAFv8/DtvEzXKf2kLf4P2DPx9T6Lq/h8z4bScG6WTiP78CV+jd5X4xDUXQYEzsVhVFzUj0WY8PrcOz7DdfpQ5WVrBJmb1bp1xj6NFJIjFJ4tqeCxQCLtnsPP2eLSsd4GNpKISFK4ZH2OhrEwicp7mt2y3G4taHC1TUV4sIU7khWSIqBbSfcYe5IVmhdQ/t9oyoKj7RXOJ4NR7MqOMGFVFVlzm/5DO8WQI+GZpKqGXj5jmCtXNtZRrm2Op9+rS30bWUhsYqBSb2DsZgUFm60FoW582oLresYqRGhp1GcgTHXBHL8jIujGVq5Hhag4+62ATSpYSQuQk+7RBN3t7Gw6YD3OpMvXan1tQsxOwVubwS3NYTESJjYDe1a2Ok9fJMq8GhHuPEqMF2+yfKgqipz5nzBiBED6dmzI/XrJ/DKK09w6lQaK1b8Wup2W7bsoEePDnTt2o4aNapy/fVd6NixFdu27S4K8+abM+jcuQ3/+c9wGjasR61acfTo0YGoqIjKSNp5qarKnM/XM3xIJ3p0TiIpsQovT+jNqbRsVvyyu8xtc/NsPDppMc8/fjOhIZ690zdvO8zRE5m89PStJCVUISmhCpOfuZXtu4+xbtP+ikyST+345Xu+eutZUpb/c3r/naWqKnN+zmH4tSH0aBJAUnUjL98TwaksJyv+8D56DmDWTzn0axdE3zZBJFY1MqlfuFaer9fqb3qdQkyo3mNZ8YeVG5oFEGTW6qT7Tjo4k6cy+voQ6sYaqVfNyMjrQknLdnHsdMU+lwjxb1Kup7wRI0bw2Wef8ccffzBmzBjGjBnD9u3bmT9/Pg8++KCv4wiAzamy47iL9sUeanSKQrs6elKOeH+jmnLESfs6npWkDgkGUo5ohcSRTJXUHJX2dd37DLEoJMfpi8Kca9UeB5n5Kn2aeTb0rdvv4LuddibcWLFDqSriOPxTHMlwaecrwX3sQyw6kmsYSDns/YHZ5lDZcczpsY1Op9AuwVjqNhdi1W47mXkqfVpUfM8Zf6bb5gCjXkGnczcOWIza/zcdLP/xO+/fdarsOKHSvo67iNIpCu3idaQcLSWfH3XRPt6zSOtQp/TwNqfK/BQnIWaoH+u53bR1Dtq8aeW2mQXMWOfAUYlvlyvzfGdbXQSbFQz6y2+YmDdH0h2kZjlpn+TuuRoSoCM53kzK/oIytrw8lV2eey+ftfLc8wm4Q93Sy/+yLNlup+1rOdwyNY/XVxaQX8Yb/AqlN6KPS8bx1y/udaqK/e/VGGq1urB9GANAb0DNy/T6ayU4GmP9nhRs8N/Hy2xOlR0noV0t9/WmUxTa1YKU496P/dbjnuEBOtaGlOPun5tXgx/3qZzMUVFVlfWHVQ5kQIfa3q/rPLvKoh0qNUKhasXNWuLhSIaL1GyV9onuXkohFh3JNQ2kHPLeIKaVaw7aJ3op1w55L9fybCqLNlmpEaGjapj3Ku7JLCfLdxTQuk7FvrC9kutr52NzasN62xUbPKRToG1N2Op9FP8/0pEjx0lNPU379i2L1oWEBNO0aQO2bNlR6nbNmzdi3brN7N9/GIDdu/9m06btdO58NQAul4ufflpHfHwNhg59jHbtbqNfvxFlNixWtiPHMklNz6F9q7pF60KCLSQ3jCNl+5Eyt33u9WV0bV+P9q3rlvidze5AUcBkdF9XZpMBnU5h01b/veC5khxJd5Ka7aL9Ve5noJAAHcm1TaQc8P5Sx+ZQ2XHE7rGNTqfQrp6ZlIPet9l+2Mauo3b6tnG/wK0TayA8SMcX6/OwOVSsNpWF63NJqGIgLvIf8nbgsqKT5QpV7kmubrvtNm677bZybVtQUEBBgecDm8nuwmws/URk5Kk4VYgK8gwTHaSwP8175SgtRyUqWCkRPi1Hq2ynFv4bFXROmGB3mHMt3GKnY4KeqqHueGTkqTzxlZVXbrNUeE+KijgO/xSphcN/ooLPSXuwjrRs72nJyFNxuiiZ/mAd+9PK3wtg4aYCOtYzlvqQ4Uv+THfbugZe/tbFjNX5DGxnId+u8voP2hu71OyLb3S4UBl5aPk8sGS+3Z/u/e+m5Xi5lr3k8x//cjL+Kzv5dogJhpl3mYgo9ncGtjLQsIpCeIDCliMu3vjZwakclSd6Vs5cI5V1vjNyXbz/o5U7WvtvCOjFSs3SyriokHOOTYietKx/3kNyUXnupXzen1ZaPle93LN0pOVeXKP8zY2NVA9TiA1W2HPKxWsrbRxIdzHlDu9TA1QkJTASRW/AlZPqsV7NTkUfk3hB+wi48RlcWSex//2L19+bWt6JWpCDffuyS45veWXmny3XPNdHBcL+DO/bpOWWHPoYFaSQVmz48NPdFCasUOk6TcWgA0WB53pqPf+K+2SryuurVfLsUCcCZvRVMFXSC4Cz9wvv5Zr3vJ6R5yos18693nXsT/Us1z5Zm89r3+WSZ4M6MXpmDg3DZPBM27hPs1i1y4bVDt0amHihT8XOH3Yl19fOpzzXwj9RauppgBK986KiIkhLO13qdg88cA85OXnccMNg9HodTqeLsWOH0qvXNQCkp2eSl5fPtGmfMmbMfTz66IOsXv07Dz88gTlz3uDqq5tVWJouVOppbdqYqMggj/XRkcGknS59Splvlm9n558n+GLGMK+/b9aoBgEWE6+9t5Kxw7ujqiqvv78Sp1MlNb3ip6oRkFo4fDcq2LPRLTpYX3p5nltYnnupv+0/5f0l7sL1eSRUMdCijrueGmzRMWdkNA/PTOf9H7S52mvHGJj+YNQ/5oW2EJeDcjUCbtiwAZfLRZs2bTzWr1+/Hr1eT6tWZb+5f+mll5g0aZLHugm3VePZvtXLE51KcyLLxa97nbx5u2dvvwlLrdzc2Ejr2pX34YArwZKUAiZ+7Z6zZ+rASuqycB4nzrj49S87b95VMQ8Ql1O661Ux8FLfIF7+No83luejU2BgOwvRwQ50/9B7bZvaOhbfZyIjHxakOBnzpZ3PB5uKGlbuvdp9HSfF6jDqYeJ3DsZ3NZR4qPQFf5zvHKvKg3OzSYjV83D3ym/0uVBLNuQw8TP3g9LU4bF+jM2/y50t3I3aSVX0xAQrDPnYyqHTLmpF/rPejJq7Poyp6a3kfNAHHN4fJsyt7sK2ZVGpv/8n+zhF6z31Xi+F6qGw8Sg8v0olNgjaF+sNeEt9aF9LITUXPtqkMvYblU/uBHNFlGtbrEz80v1APnVwxX5g6JbmZtrXM5Ga7WLm6nzGfJLFp8PDMRvdaXvi5mAe7uHiQJqTN77PY/I3uUzs/c/5kIC4/H399XImTnyj6OcPPnipXPv59tufWLJkBa+//jSJifHs2vU3L730LrGxUdx22/W4XFpDS48e7RkypB8ADRoksnnzDj77bIlfGgGXfP8HE19xz7c69bW7L3ofx0+e4cW3vmfm2wMwm70/U0VGBPHWC7cz6dVlzF2wHp1O4aaejWmYVM1j1IrwnSWb8pj4eWbRz1Pvv7APu1wKq01l6aY8RlwbUmL9059l0DzexOsDg3G6YOaP2Qyfls6CsbFYKnH6HiH+ycrVajVy5Ej+85//lGgEPHr0KC+//DLr168vc/snnniCcePGeawzLepa5jYRgQp6hcLJlN1vHtJyVaKDvT+wRAcrpJ/z9lQLrxUQMYX/pueqxBYrY9JyVBpULbnPRSl2wgMUul/ledjW7Xew6k+YuVbrzqwCLhUaPZ/Nczdb6Nvcdz2IKuI4XK66NTCRXGyuOlvhx1rSc1zEFnuTlJbjokE1713AIwIV9DpKpj/HVerxOp9FmwsID1ToXr9ieoZdbum+pamZW5qaSctxEWBUUBRt/rmaFdjtPiIQLZ/nXXi+jQ6mxEdDvIUPNCnUjlSoDTSL03Hd1AK+2Orkwfbei8Pk6jocLjhyRqVulO+vmco+3zkFKsNmZxNkUvjfPcEYL+M3p92aBJIc734DXHRssl3EFmtTSMt20iDu8pkM/UIVlecXUT5HBysl83mOi+igSzuPZ79WejCj8hsB1bzTqE4HuuAYivePUkJicGWfKnNbc+cRWLqOImfaHThP7PIaxhDfBn1sPXI/qZjpSi5UeMDZcs1zfXpe6R86iA7SvpzqET5XLQpvdai89ZvKO7codK2r5YGkGNiVqvLRJtWjETDErBBihvgIaFoN2r6nsuJvuKm+r1Lo1q2hieSa7t5PNmexci303HLNe9kbEagrLNc8e5akZbuIPqc3SYhFR4gF4qP1NK1poM1z6SzfYePmZu7y4+ycvnVjDYQF6uj/wRlGdA/0iI8vXUn1tYtVnmvhn6B79w40bdqw6GebTXsuSE/PIDbW3XCSnp5B/fql93J+5ZWpPPDA3dx0U3cAkpLqcuzYST744BNuu+16IiLCMBj0JCTEe2yXkFCLTZv+8GGKLly3jleR3MhdxtpsWu/09NO5xEa7H7LSTufQoJ73T0Dv2H2c9Ixc+tz7YdE6p1NlY8pB5i38nW0/PYVer6NjmwSWfzGKjMw89HodoSEWOt78OjWrN6qg1F3ZujWykPyo+yWsu57qJDasWNmW46RBde/PRhFBheV59rnluZPo0JJ12++35mO1q/Ru7VkgLN2cx9HTTj57JKao0fe1gZG0eeo4K7fnc1OLf3ABIkQlKlfNZ+fOnbRo0aLE+ubNm7NzZykz+hZjNpsJDQ31WMoaCgzaBzoaVdOxdr/7EcGlqqzb76RZDe/bNquhZ+1+zyFSa/Y5aFY4UXKNcG3S/+L7zClQ2XbUWRTmLFVVWZRi59ZkQ4mH5s/uC2Txg+5ldFcTQSZY/GAgPev7tndgRRyHy1WwWaF2lL5oSYzVequs3eseBpRjVdl2xEGzMj7o0ai6nrX73Nu4XCrr9tlL3aYsqqqyaHMBtzYzV1jjyeWYbtCGbQWZFb79w4bZAO0TKq7nq0mv0KiqwtoD7sqCS1VZd9BFs7hS8nmcjrUHPSsXaw6UHt69X/cDqje7T6rolJJDk32lMs93jlVl6KwsjHp4b0CIRy+Zy1GwRUftGGPRkljVSEyonrV/uj8KkJPvYtuBAprV+ecMaz6rqDw/4K08914+a+W551DCNWWU/xdq90nt2on1R2OD047z6DYMxT7qgaJgTOyI49DGUjczdxlJQI+x5My8G+fRraWGM7W+B8eRrTiPn79+UpFMeoVGVWDdYXd541JV1h2GZtW8H/em1WDdIc/yac0haFZN+7/DCXYXJXpm6xWtbCuVqr2wtFXQKPpgs47a0fqiJTFWT0yIwtq97rmfcqwuth120KyW94dGrVwzeJSFLpfKur12mtUq+/6jpa30A3D22JQV5lJdSfW1i2XSQ8NYWFdsajiXCusPQ1Pv7UP/CMHBgdSuHVe0JCbGExMTydq1m4vC5OTksnXrLpo3L73BymotQFE884her0NVtfxqMhlp0qR+0ZyBZx04cIS4OM+v6VaW4CAztWtEFi2JdWKIiQpm7Ub3xzpycgvYtvMozRrX8LqPtq3q8PXc4Sye9WDR0rh+dW65tgmLZz2IXu95TCLCAwkNsbBu437SM3Lp1vGqCk3jlUqrixmKlsSqBmJCdKzd4+5Zn2N1se2gjWbx3l/ImgwKjWoYPbZxuVTW/VVAs9olt/lifS7dGlmIPGfIcb5Nq5MX/yajTgGF89zzhBAeyvUUbzabOXnyJHXrek7Yevz4cQyGimsYGNLOxONfWmlcXU9ydR2z19vJt7s/0vHfL/OJDdExvof2MDiwjYlBs/OYudZG13p6vtnuYMcxF8/drA3nVRSFQW2MTF1dQHykjrhwhXd+shEbopRovFu338mRTJV+LUpWVhNiPAuo7cdc6BS4KrZiKm2+Pg4Amfkqx8+4OFU459jZedeig7WvR4I2V1lajsqh09rv9px0EmRWqBamfTW5oimKwqD2Fqb+ZCU+Sk9chI53Vmpp7dnAfQMZMjOLng1NDGirpW9IBwuPL8ylcXUDyTUMzF5jJd8GfVq6Gw1Ss12k5bg4VPhlKS1tLi1tge5Kx7p9Do5kuOjXqvIaHPyd7o/XWWley0CgSWHN33Ze/T6PcdcGEhpQsb2Fhlxt4PGldhpX1ZFcXWH2Bif5duiTrF1X/12iXavju2r5fmArA4Pm2Zi53kHXRB3f7HSy47jKczdo4fNsKlPXOOheT2tky8hT+WSzk5PZKtfX18JsOeJi2zEXbWrrCDJBylGVl1bauaWRjrBKyONQcef7bANgvh1evSeInAKVnALteo8MUtAXtiIcTHeSZ1NJy1GxOlR2HdceSBNi9BUyHPpiKIrCoK4hTP3+DPGxBuKiDLyzNJPYMAM9k91vf4dMOUnP5AAGdAkFILfAxaFU94P1kXQHu47YCAvUUT3Sv9M4DGlr5PGvCmhcTUdydT2zf7dp5XlTLV7//dKq5fOz5fnVRgbNyS8szw18s8Oulec3ua/r85Xnh067WLrdQed6esIDFPacdPHS8gJa1dKRVMU/jQ3W1R8QdMfbOI9sxXFkC5aO94MxENvGzwAIvGMKrqzjWL97EQBzl4cJuPYxcj99CNfpwyjBMQCotlywFeteZA7GlHwLeUufrewkeTW4hcIT36s0jlVpUlX7+m++HW4rbA/473cuqgTDuI5a+TqoucKgBVqvvi51YNmf2sdFJvXUrsVgs0LrGiqvrlaxGKB6KGw4Al/thP920cIczlT5dg90qA0RAXAyB6ZtUDEboHOdykm3oigM6hDA1FX5WrkWqeed5XlaudawWLk2/YxWrrXXpikY0imAxxdk0zjOQHJNA7N/s5JvU+nTUiv3Dp92smxbAR3qmYgMUjhxxsW0n/MxGxS6JGn7/Xm3jbQcF01qGAg0K/x90smr3+bSoraBGhEVm9+v1PrahRjcDJ5cAY1itS//zk2BfIf2tWCAJ36A2GAY21772eaEvYWzQ9hdcCoHdqVCoBFqh/shARdAURQGDbqd99+fS+3acdSoUY23355JbGw0PXt2LAo3ePA4rrmmEwMGaHOtd+vWjqlTP6Z69VgSE+uwa9dffPTRAvr2vaFom6FD72Ts2Odo3TqZNm2as3r17/z44xrmzHmrspPplaIoDLqjDVNnrya+ZiRx1cN558OfiI0OoWdnd/fjIaPm0LNLfQbcfjXBQWauSvCc9iMgwEh4WKDH+oVLU0iIjyYyPJCU7Uf4v7e+Z/CdbalbO7rS0nepzIFBxNROKPo5ukY8NRokk5uZQcbxw2Vs6X+KojCoSzBTl2cTH2MgLtLAO99mERuqp2cT9xQzQ95Lo2cTCwM6adMuDOkazOOfZNC4ppHk2iZm/5yjledtPHvvHUx1sHGfjQ+9DDvukGTm1SVneG7hGQZ0CsLlgmkrs9HroE3iP+9lsN8pl8f9QFS+cj39XHvttTzxxBN89dVXhIVpY7IyMzN58sknueaaa3waweJubGTkdK7KlJ8KSM1RaVBFx7R7AouGVRw7o6Io7p5ALWrqea2Phbd+LODNVSrxkTr+d2eAR+PcsPYm8m3avH5ZVpWWtfRM6x9YYo6cL1LsNK+ho260/9/GVsRxWPWngye/dveuGbdQ+//IziZGddUK1c822nn3F/db/AGztc/Av9jLUuJryRVlWCcL+TaVCV/lFp4vA9MGe/ZoOnTaRUauO/03NjFrx2tlPqmFQyqnDQ7xGI7z2e9W3v3Rnf4B07XJZl/sE+TxBeAvNhXQvJaBujGVmw/8me4/jjiYsjKfPJtK3Rg9k3oFcWvzir/R3thQz+k8lSmr7aTmQoNYhWl3mIqGPR7LUj3uXS1q6Hitl5G3fnHw5s8O4iMU/tfXyFUxWnr1OtifrjL6DxsZ+dpwpCbVdMwbYKJeYRiTAZbtcvK/Xx3YnFAjTGFwawP3Xv3PP987jjnYWviFyWvfPOPx91aMDyt6GH56cS4bDrgbzG57N6tEGH8a1jNUOzafppOV76JlXQvTHor1PDZpdjJy3Xl0+yEbg985WfTz5MXazPO9rw5i8kD/PjTc2Mio5fOfbcXK8wB3eZ7l8ugR0qKmntduKyzPf7Rp5fkdFs/yfI+DJ792v20ft0j7/8jORkZ1MWPUw5r9Dq3B0QbVwhSurW9gRCf/Dam2b/uK/KAoLNf+B11IDM5jO8iZeTdqThoAuvA4UN353dx2MIrBTPDAGR77yV/+GtYVrxX9bGraGwDb1sUVn4gLcGOSQkY+vLNWJS0PGsTAh7cpReXa8WzPXn3Nqyu8egO8vUblzd+0xo4pvRSuinYHev1GhTd/VXnsW5UzVq0hcEwHhbuStd+bDbDxqMqcLZBl1T6+0KoGfHqnUmE9nL0Z1jlAu3YX52jlWm0j0+4N87x2051k5BUr15LNnM5xMWVFHqnZ2tDhafeGFg0HNhlg0347c37LJytfJSpYR6t4I5+OCCv6oIjZCAs2WJn8jRObQ6VqmI5rG5u5v0vFz4d6JdfXzueGq+B0PvxvvfYBnPox8EEv93Dg4zmez6epuXD7Z+6fP9qiLa3jYFafyo37xbj//rvIz89nwoTXycrKoWXLJkyf/jJms7u8PXz4GBkZ7vvy00+P5u23ZzJp0tuFQ4mjufPOWxg5clBRmGuu6cSzz47lww8/4YUXplCnTk3eeWcSrVo1qdT0lWXYgPbkW21MeHkpWTlWWibXYtob/T3m+zt0NIOMzLwy9lLSgUNpvDl1JWey8qleLZzhgzsy5K62vo5+harduCXjP15e9PMdT74KwJpFc5j9+P3+itYFG9Y9WCvPP8/U6mJ1zEx7MOqcupjDs57aPFArz7/LJjXLSYM4I9MejCY6xLNuufD3XKqG6emQVPJZo24VI+8Pi+Ld77O5661UdDpoEGdi2oPRHkOThRBlU9SzfcsvwtGjR+ncuTPp6ek0b94cgJSUFKpUqcLy5cupWbPmRUdEnVf2x0TEv4zp8qiEikqSe3EVvH+NoCt0bpKwy+MjPpXuVLq/Y+AXmX8c9XcU/CKsTsT5A/0LKbGR/o6Cf+T/+z4qcyGcGdn+joJfGB7+0d9R8As1/cpM9/B29/k7Cn4x9a0O/o6CXyg3rvJ3FC4P9g3+joH/GVv7OwZ+Ua6egHFxcWzbto158+axdetWAgICuPfee7n77rsxGqVxRwghhBBCCCGEEEKIy0m5GgF/+eUX2rdvzwMPPOCx3uFw8Msvv9C5c2efRE4IIYQQQgghhBBCCHHpyjWzf7du3Th9+nSJ9WfOnKFbt26XHCkhhBBCCCGEEEIIUREUWa5Q5WoEVFUVxcvXZNLT0wkKCrrkSAkhhBBCCCGEEEIIIXznooYD9+mjfX5LURSGDBmC2ez+ao/T6WTbtm20b9/etzEUQgghhBBCCCGEEEJckotqBAwLCwO0noAhISEEBAQU/c5kMtG2bVvuv//y/6y5EEIIIYQQQgghhBBXkotqBPzoo48AiI+P59FHH5Whv0IIIYQQQgghhBBC/AOU6+vAEydO9HU8hBBCCCGEEEIIIUSFK9fnIcS/QLkaAevUqeP1wyBn7du3r9wREkIIIYQQQgghhBBC+Fa5GgHHjBnj8bPdbmfLli189913PPbYY76IlxBCCCGEEEIIIYQQwkfK1Qj4yCOPeF3/7rvvsnHjxkuKkBBCCCGEEEIIIYQQwrd8OhD8hhtuYOHChb7cpRBCCCGEEEIIIYQQ4hKVqydgab744gsiIyN9uUshhBBCCCGEEEII4TOlf+NB/LuVqxGwefPmHh8GUVWVEydOkJqaynvvveezyAkhhBBCCCGEEEIIIS5duRoBe/fu7fGzTqcjJiaGrl27Ur9+fV/ESwghhBBCCCGEEEII4SPlagScOHGir+MhhBBCCCGEEEIIIYSoIOWeE9DpdPLll1+ya9cuABo1akSvXr3Q6/U+i5wQQgghhBBCCCGEEOLSlasR8O+//+bGG2/k6NGjJCUlAfDSSy9Rs2ZNvvnmGxISEnwaSSGEEEIIIYQQQgjhA4p8GORKpSvPRqNHjyYhIYHDhw+zefNmNm/ezKFDh6hTpw6jR4/2dRyFEEIIIYQQQgghhBCXoFw9AX/++WfWrVtHZGRk0bqoqCgmT55Mhw4dfBY5IYQQQgghhBBCCCHEpStXT0Cz2Ux2dnaJ9Tk5OZhMpkuOlBBCCCGEEEIIIYQQwnfK1Qh4880388ADD7B+/XpUVUVVVdatW8fw4cPp1auXr+MohBBCCCGEEEIIIYS4BOVqBHznnXdISEigXbt2WCwWLBYL7du3JzExkbffftvXcRRCCCGEEEIIIYQQQlyCcs0JGB4ezldffcXff//Nzp07AWjYsCGJiYk+jZwQQgghhBBCCCGE8CX5OvCVqlyNgAAzZszgzTff5K+//gKgXr16jBkzhmHDhvksckIIIYQQQgghhBBCiEtXrkbACRMm8MYbbzBq1CjatWsHwNq1axk7diyHDh3iueee82kkhRBCCCGEEEIIIYQQ5VeuRsD333+fadOmcffddxet69WrF8nJyYwaNUoaAYUQQgghhBBCCCGEuIyU68MgdrudVq1alVjfsmVLHA7HJUdKCCGEEEIIIYQQQgjhO+VqBBw4cCDvv/9+ifUffvgh/fv3v+RICSGEEEIIIYQQQoiKoJPlCnVJHwb54YcfaNu2LQDr16/n0KFDDBo0iHHjxhWFe+ONNy49lkIIIYQQQgghhBBCiHIrV/Pn9u3badGiBTExMezdu5e9e/cSHR1NixYt2L59O1u2bGHLli2kpKT4OLpCCCGEEEIIIYQQQlx+Tp8+Tf/+/QkNDSU8PJyhQ4eSk5NTavgDBw6gKIrXZcGCBUXhvP3+s88+u+j4lasn4I8//liezYQQQgghhBBCCCGE+Ffq378/x48fZ/ny5djtdu69914eeOABPvnkE6/ha9asyfHjxz3Wffjhh7z66qvccMMNHus/+ugjrr/++qKfw8PDLzp+5R4OLIQQQgghhBBCCCHEP01BQQEFBQUe68xmM2azudz73LVrF9999x0bNmwo+pjulClTuPHGG3nttdeoXr16iW30ej1Vq1b1WLd48WLuuOMOgoODPdaHh4eXCHuxFFVV1Uvag4+oc1v4Owr+4XT6Owb+cXlku8rnuELPd3iov2PgH1mld/v+V0uo7e8Y+EdOrr9j4B9hIf6OgX+cTPN3DEQlOvjtdn9HwS9q39bS31HwC+eBY/6Ogl8oiuLvKPiFrk7Jh/IrwfAxv/k7Cn7xwZ6C8we6EjiuzPtacc++8AWTJk3yWDdx4kSeffbZcu9z5syZjB8/noyMjKJ1DocDi8XCggULuO222867j02bNtGqVSt+++032rdvX7ReURSqV69OQUEBdevWZfjw4dx7770XXXZLT0AhhBBCCCGEEEIIccV44oknPD5qC1xSL0CAEydOEBsb67HOYDAQGRnJiRMnLmgfM2bMoEGDBh4NgADPPfcc3bt3JzAwkB9++IGHHnqInJwcRo8efVFxlEZAIYQQQgghhBBCCHHFuJihv48//jgvv/xymWF27dp1yXHKz8/nk08+4Zlnninxu+LrmjdvTm5uLq+++qo0AgohhBBCCCGEEEII4Qvjx49nyJAhZYapW7cuVatW5dSpUx7rHQ4Hp0+fvqC5/L744gvy8vIYNGjQecO2adOG559/noKCgovqwSiNgEIIIYQQQgghhBBCeBETE0NMTMx5w7Vr147MzEw2bdpEy5bavLqrVq3C5XLRpk2b824/Y8YMevXqdUF/KyUlhYiIiIsewiyNgEIIIYQQQgghhBBXiiv0Q0AVrUGDBlx//fXcf//9TJ06FbvdzsMPP8xdd91V9GXgo0eP0qNHD+bMmcPVV19dtO3ff//NL7/8wrJly0rsd8mSJZw8eZK2bdtisVhYvnw5L774Io8++uhFx1EaAYUQQgghhBBCCCGEuETz5s3j4YcfpkePHuh0Ovr27cs777xT9Hu73c6ff/5JXl6ex3YzZ86kRo0aXHvttSX2aTQaeffddxk7diyqqpKYmMgbb7zB/ffff9HxU1RVVS8+Wb6nzm3h7yj4h9Pp7xj4x+WR7Sqf4wo93+Gh/o6Bf2Tl+DsG/pFQ298x8I+cXH/HwD/CQvwdA/84mebvGIhKdPDb7f6Ogl/Uvq2lv6PgF84Dx/wdBb9QrtCeQbo61f0dBb8YPuY3f0fBLz7YU+DvKFwenDv8HQP/0zfydwz8QufvCAghhBBCCCGEEEIIISqWNAIKIYQQQgghhBBCCPEvJ3MCCiGEEEIIIYQQQlwxpD/YlUrOvBBCCCGEEEIIIYQQ/3LSCCiEEEIIIYQQQgghxL+cNAIKIYQQQgghhBBCCPEvJ42AQgghhBBCCCGEEEL8y8mHQYQQQgghhBBCCCGuGIq/IyD8RHoCCiGEEEIIIYQQQgjxLyeNgEIIIYQQQgghhBBC/MtJI6AQQgghhBBCCCGEEP9yF90IWLduXdLT00usz8zMpG7duj6JlBBCCCGEEEIIIYQQwncu+sMgBw4cwOl0llhfUFDA0aNHfRIpIYQQQgghhBBCCFER5MMgV6oLbgT8+uuvi/7//fffExYWVvSz0+lk5cqVxMfH+zRyQgghhBBCCCGEEEKIS3fBjYC9e/cGQFEUBg8e7PE7o9FIfHw8r7/+uk8jJ4QQQgghhBBCCCGEuHQX3AjocrkAqFOnDhs2bCA6OrrCIiWEEEIIIYQQQgghhPCdi54TcP/+/UX/t1qtWCwWn0ZICCGEEEIIIYQQQgjhWxf9dWCXy8Xzzz9PXFwcwcHB7Nu3D4BnnnmGGTNm+DyCQgghhBBCCCGEEMJHFEWWK9RFNwK+8MILzJo1i1deeQWTyVS0vnHjxkyfPt2nkRNCCCGEEEIIIYQQQly6i24EnDNnDh9++CH9+/dHr9cXrW/atCm7d+/2aeSEEEIIIYQQQgghhBCX7qIbAY8ePUpiYmKJ9S6XC7vd7pNICSGEEEIIIYQQQgghfOeiGwEbNmzI6tWrS6z/4osvaN7tmgMcAABtjklEQVS8uU8iJYQQQgghhBBCCCGE8J2L/jrwhAkTGDx4MEePHsXlcrFo0SL+/PNP5syZw9KlSysijkIIIYQQQgghhBBCiEtw0T0Bb731VpYsWcKKFSsICgpiwoQJ7Nq1iyVLlnDNNddURByFEEIIIYQQQgghhE/oZLlCXXRPQIBOnTqxfPlyX8dFCCGEEEIIIYQQQghRAcrVCHg5mbfRzoy1dtJyVOpX0fH0dSaS4/Slhv9up4O3f7ZxNFOldqTCoz1MdEnUDoPdqfL2T3Z+/tvBkUyVYLNC+zp6xnU3UiXE3VLcfUoex86oHvsd183IAx1MFZPICzBvk4MZ6x2k5UD9WIWnrzWSXL301u3vdjl5+xcHR88UHoeuBrokej9uE7+zM3+Lkyd6GBh8tX+zzLxNTi2duYXpvMZQdjp3O3n7F2exdOrpklBGOlNcPNFDz+DWnun86W8n7/3m5M9UFbMeWtfS8W5fo0/TVpZ5W1zM2ODS0h0DT/fQk1xNKTX8d3+6ePs3F0fPQO0IeLSzji51PY/T3nSV135xseGwitMFCVHwzq16qoe697vlmMpbq11sO66i00GDWJjeV4/FWPrf9iVVVZmyMp8FGwvIsqq0qGVgYq8g4qNLv8YB5q2zMuNXK2k5LupX1fP0zUEk19DOaWaeiymr8vntbzvHM11EBuno0cDIIz0DCLFox2j3cQcf/mJl80EHGXku4iJ03NXawqD2lgpPM8C8zU5m/O505/OeepKrlZXPXbz9q6PwfCs82kVPlwR3+MeXOfhyu8tjm451FKb3c+fhEQvt7D6lkp4HYRZoV1vH+C56qoRUzrkujaqqTFlymgWrz5CV76JFgoWJ98QSX6X08nbDnnxm/JDBjkNWUs84+d+IavRsFlxq+InzTjL/lyye6BfN4J4RFZGMMqmqypTvs1mwLk9LYx0TE/uGEx9Tdnk779dcZvyUQ1q2k/rVjTx9WxjJtTyPy5YDNt76Notth+zoFGgQZ2T6A1FF1/D+VAevLsli834bdqdKUjUjo28IoW2iucLSWxZVVZnyVToLVmeSleeiRWIAEwdUOc/5zmPGd6fZcbDwfI+sTs/mIR5hHp95nC/XZHms69gokOlja1ZIOspSVK5tsGrlWm3jhZdrq/MLyzUDT98cSHJN7RrOzHMxZWU+v/1tc5drDU0e5RrAH0ccvP59LjuOOVGAJjUMPHZ9IPWrVfy93Z/pPisjz0XvKWc4meXi96cjCA2o/F4AId3uJ+z60ejDqmA7vJ30Tx7Dtn+T17CBLW4h7KbxGGPrgt6I4+RezvzwP3LXflYURhcaQ+Ttz2Fp1B1dQBgFe9aQ/sljOE7trawkeVUR5dqR0w56/t8pr9u9NSiC65sGkJHr4rF5Gfx53E5mrouoYB3dG1sYd2MowV7yhD98sg0+2gxpeZAUDU92huSq3sP+nQ5T1sPOU3AsG/7bCQY1q9ToXpBPtqrMLJamp7pActUy6ql/qUxZB0ezoHY4jOsAXeLd4dPyVN74DX47BNkF0Ko6PNkV4sPdYSauUll3CE7lQqARmlWD8R2gbmTl1VtUVWXKd9ksWJtLltVFi3gzE/tdSD7PYcaqYvm8TzjJtYvl8+dPet3urcGRXN8sAIA/Dtl4fWkWOw7bUBRoUsvEY7eEUT+u8p5PLla9Vh25dtg4ajVqTniV6rz3UD+2rvja39ES4opz0XfDiIgIIiMjSyxRUVHExcXRpUsXPvroo4qIawnLdjiYvNzGyE5GFg0LIKmKjmGfWknPVb2G33zYyfjFBdzezMDi+wPomWTg4c8L2HNKezi22mHnCScPdTKxcFgAU243sz/dxUOfF5TY1+guRlaPCShaBrT2X4G7bKeTySsdjOxoYNF9Ju04zLeVfhyOuBj/lZ3bm+pZfJ+JnvV0PLzQzp5UV4mwy/90svWoi9jSn58rzbJdTiavKkznvUaSYhWGzbefJ50Obm+qY/G9xsJ0OkpP5zHVazq/3+3kv0sd9EnW8+V9Rj4ZaOTmhpVXkVy228Xkn1yMbKdj0UC9lu4vnKWn+6jK+KUubm+sY/EgPT0TFR7+0sWeVHf4Q5kq93zqpG4kzLlTz1dD9DzUToe52LPYlmMq93/hpEO8wucD9CwYoKd/Mx26SmwTmr7aytx1BTx7axCfDw8lwKQwbHY2BXbvaQdY9kcBk7/NY2S3ABY9FEZSVQPDZmWTnqOd91PZLk5lufjP9YEsGRXGS32CWP2XnacW5xbtY8cxJ1HBCq/0C2Lp6DCGdwngjeV5fLzOWuFpXrbLyeQfnYzsoGfRYCNJMQrDPneUcb5djF/i4PYmehYPMdKznsLDi0vm8051FFY/ZCxaXr/Fs4LappaON2818O0wI2/3NnAoU+WRrxwVls4LNf37DOauyuTZ/rF8/nhNAsw6hr1zlAJ7yev4rHybi/o1TEy4O/a8+1++JYet+6zEhpfdEFGRpv+Yw9zVuTx7exifPxJDgEnHsA/Ty87nW/KZ/PUZRl4bwqKxMSRVNzLsw3TSs51FYbYcsHH/tHQ6XGXm80eiWTAmhv4dgjyu4eHT03G6VGaPiGLh2BjqVzcwYsZpUrOcXv5qxZv+3Wnmrszg2QFV+PzJWtr5fvNI2ee7wEX9mmYm9K9S5r47NQ5i9esJRcvrD1T3dfQvyPTVVuautfLsrcF8PiKMACMMm5VV9vneVsDkZbmM7B7AopFhJFXVlyzXsl385/oglowO56W+wazeY+OpRe5yLbdAZdisLKqF65k/PIx5D4QSZFYYNisLu7P0v/1PT3dxTy/KIamq/671wNZ9iLzzRTK/nsyxSZ2wHf6DKmMXoQuJ9hrelZvBmaWvcfzFnhyb2J7s3+YRfe97WBr1KAoT+/CnGGLiOTXlbo5N6ogj/RBVH/0KxRRYWcnyqiLKtWrhelZPrOKxjLouhECzQqf62osLnQI9Glt4775Ivns8lpfuimDtHhsTvzhTKek+n2/3wCur4aGrYcFdWoPZg19Dep738PkOqBkKY9tDtH9Paam+3aPy8mp4qA18cRfUj4YHvoL0PO/nestxlce+gz4NYeHd0KMujFoKf6Vr4VVVZdRSOHwG/nezFqZaKAxdDHnF8k+jWPi/a2DpQJjWW1s37Etwuiq+PDtr+qoc5v6Sw7P9wvl8TCwBZoVhU9POk8/zmPzlGUZeF8Ki8bFaPv8gzTOfT6rqsYy6vjCfN9DyeW6Bi2EfpFEtQs/8sTHMGxWjlecfpFVKeV5epsAgjuzexqfPPeLvqAhxRbvolowJEyag0+m46aabmDRpEpMmTeKmm25Cp9MxcuRIrrrqKkaMGMG0adMqIr4eZq2306+5gb7NjCTG6Jh0owmLUWFhit1r+Lkb7HRM0DO0nYmEaB2PdDXRsJqOeRu18CEWhZn9A7ihoYG6UTqa1dDzzPUmdhx3ceyM5wNIkEkhJlhXtASa/NdbZtbvDvo11dM32UBitI5J1xuwGGDhNu8PcXM3OuhYV8fQtgbtOHQx0rCqwrxNnuFPZqu8sNzOq72MGPxXZy4y63cn/Zrq6Jusd6fTWFY6nVo62xSms7Oh9HSucPDqLQYM51wRDpfKiysdPNbNwF3N9dSJ1JEYreOGBpV3QGZtdNGviULfJjoSoxUmXaPT0r3d+01+7mYXHesoDL1aR0KUwiMd9TSsAvNS3Hn4rdUuutRVeKyLnoZVFGqFK3RP1BEV5M7Hk390MrCFwgNtdNSLVqgbqXBDfR0mQ+X1ApyzxsrwrhZ6NDCRVNXAy7cHcSrbxYpdtlK3m/WblX6tzPRtaSYxVs+kXoHa8dqkNeZfVcXAlHtC6F7fRK0oPW0TjIy9JpAfd9txFFac+rY089RNQVxdx0jNSD29mpnp08LM8h2l/11fmbXRRb9kHX2b6LXzfZ1ei/8f3htB5m4sPN9t9Nr57mSgYRWFeZs9w5v0EBOsFC1hFs/zOKS1nmbVdcSFKbSI0/FAGz1bj6l+rUyqqsqclZkMvzGSHs2CSaph5uV7q3Aq08mKFO8P+QCdGwcxpnc01zQv++3FyQwHL3yWyqtDq2LQ+6cMV1WVOb/kMrxnCD0aB5BU3cjLd4dzKsvJiu2lNzrP+iWHfm0D6Xt1IIlVjUzqG6bd/353P0lO/uoMAzsG8UCPEOpVNVI31sANzQKKruGMHCcH05zc3z2YpOpG4mMMjLsplHybyl8nKr8BWFVV5qzIYPjNUfRoHkJSTQsv31eVU5kOVmzJKXW7zk2CGXNbDNe0CCk1DIDJoBATZihawoIq/8amqipzfstneNcAejQsLNf6BV9EuWYhMdbApFuDvJdrDc4t12xF5dq+VCdn8lVG9wigboyeelUMjOweQFqOyrHM0htZ/+npPuvT9VoPxPs6BlRoWssSdu3DZP8ym5zf5mE//ifpc8eg2vIJ6TjQa3jrn7+St2Up9uN7cKTuJ3vF+9iObMdSrx0AhiqJWBKuJn3uWGwHNuM4+TfpH49FMQYQ1Ob2ykyah4oq1/Q6hZhQvcey4o98bmgaQJBZq8CFBeq4u30QTWqaiIs00O4qM3d3CGTTvpIv9P1hdgrc3ghuawiJkTCxG1gMsGin9/BNqsCjHeHGq7T7+OVo1hbo1xj6NFRIjFKY2L3sNM1NgY61YWhLhYRIhdHtFBrGwLyt2u8PZsLWEzChGzSpolAnQmFiNyhwwLI/3fu5o7FCqziFuFCFhrEKo9vBiRytd2FlUFWVOT/nMPzaEHo0Kczn90Ro+fyP/FK3m/VTDv3aBdG3TZCWz/uFYzEpLFxfVj63ckMzdz7fd9LBmTyV0deHUDfWSL1qRkZeF0patotjp/3zEu9C7Pjle75661lSlkvvPyH86aIbAX/99VdeeOEF5s6dy6hRoxg1ahRz587lhRdeYNOmTUybNo1XX32Vd955pyLiW8TmVNlx3EX7Ou47ok5RaBevJ+Wo98psyhHP8AAd6upJOVJ65TfbCgoQes4D87Q1dtq8nstt0/KZsdaGoxLfOhVnc6rsOKHSvo77VGrHQVf6cTjqon2856nvUMczvEtV+c8SO0PbGKgX4//hE0XpjPeWTu/HPuWYi/bxnudNS6c7fFE6r9Z7TefOEyons0FR4LaZNjpNKeD+z21eexNWBJtTZcdJaF/bnQ6dotCulkLKsdLSrXqEB+gQ7w7vUlV+2qcSH6Ew9Asn7d91cMfHDlb85U5Teq7K1uMQGahw1ycOOrznYMBnDjYdqbx8fiTDRWqOSvsEdy/bEIuO5BoGUg57b6CwOVR2HHN6bKPTKbRLMJa6DUC21UWwWSmzISjbqhIWWLENRaXm89o6Uo6Vcj0f83Y9l8wfvx9Waf8/G9dPs/HsDw4y8ks/l5n5Kkt2umgep2D0U+MYwJE0B6lZTto3cHd/CAnQk1zHQsq+S+uV6XKp/OejEwy9Npx61f0z9BXgyGknqdku2l/ljkNIgI7kWiZSDnpvHLE5VHYcsdO+nnsbnU6h3VVmUg5qL7XSs51sPWQnMljHXe+k0mHiCQa8m+bxIBwepKNOjIGvNuaTV+DC4VSZvzaPqGAdjWpUfu/2I2l2Us+cc74D9STXtZCyt/QHqgv1+595tB/7N9c/tY9n554gI6fyH5TKLNcOlVWuOWif6B4SrdMptEs0kXLI+0tP0Mqs4uVanRg94YEKX2wqwOZQsdpVFm4qICFGT1x4xd7n/ZlugL9POXhvVR4v3x6M4q8iTW/EVLsZ1l0/utepKtadP2FOuPqCdmFp0AVj1XpY9/wGgGLQjo1qL9bApaqojoKihkJ/qKhy7VzbD9vYdcxB36tL7yJ38oyT5X9YaZ3gv3L+LJtTG9bbrtgsBDoF2tbUGr3+iWxOlZ2ntDScpVMU2tWElOPet0k57nkMADrUdh8DW2HRXHx0ik5RMOlhcyn7zLOrLN4JNUKhatnvg3zmSHop+by2iZQD58nnV52Tz+uZS702th+2seuonb5t3Pm8TqyB8CAdX6zP08pzm8rC9bkkVDEQF3mZthaLy5AiyxXqomt933//PT179iyxvkePHnz//fcA3Hjjjezbt6/UfRQUFJCVleWxlDXUx5uMPBWnikfPJYDoYIW0HO8Pt2k5asnwQQppud7/doFD5bVVNm5qpCfY7N5uYGsjr99mZs6AAO5sYeCD3+y8urLiewh5k5GHdhwCvaSr1OPg5bidE37aWid6BQa2ujxuJEXp9Hr+LjKdxcJPW+dErys9nYcztbDv/upgeHs97/czEmpRGPSJncwyGlF8JSO/tHRTerpzIeqc+nB0oEJaYcep9DzIs8O09S46xSvM6KenZz0do75y8fthbZ+HC0fN/G+Ni35NdEzrq6dRFYUhC5wcyKichsDUwuFeUcGexVR0sI60bO9xyMjT5jeMCj63XNCRluP9Os/IdfH+j1buaF36A8LmQ3a+/cPGHa0qdk5A9/XsuV4739638Xq+z8nnneoovHyjgY/uNPBoVwMbDrt4YIGjxJCZ135y0PxNG22n2DmWrfJuH//OAZqapTUORIV6Xp/RoXrSzlxaT7Vp32eg1ykM7B5+Sfu5VKlZhfk85Jx8HqIjrZQhuRm5Li2fh5xzXIJ1pBUOJzpc2BPgfz9k069tENPuj6JRDSNDpqZzIFU7doqi8NHwKHYetdPyqRM0ffw4s37JYdr9UYQFVv7Ln9QzWpyjQj3zXXSogbQzl9Zg16lxEC8PrcZH42vy6O0xbNiTzwNvHanUYWMAqdlllGullVGllmul3+czcl28/1M+d7R2l1nBZoU5w0JZklJAs2dP02LSaVbvsfHh4JAK7wnrz3TbHCrj5+fw2A1BVPfjsH99SBSK3oAzK9VjvTPrFPqw0oeyKwGh1Hr3GLU/SKfKIws4/cljWHdqDYn2E3twpB8iou9EdIHhoDcSesMYDJE10IeVMslcJaiocu1cC3/PI6GKgRZ1Ss4ZOm5uBs0eP06X504SbFZ44Y7wcqTEtzLzvd/jowK1ufT+ic6m6dyhymWlKS3PWz3VXc+pEwHVQuDNNXDGqmJzqkzfqHIiB1LPqQt9uk2l5fsqrd6H1Qdhem8wVdLLy9TCfBkVfG6e1ZOWXXqdU8vn514b+lKvjYXrz+Zzdz012KJjzsholmzKo9l/jtHi8WOs3l3Ahw9E+W1kgxDin+Oin/AiIyNZsmQJY8eO9Vi/ZMkSIiMjAcjNzSUkpPTXMC+99BKTJk3yWDehd1We7VPtYqNTYexOlTELtTerz97o2Thwb1v3m+ykKjqMepi4zMb4bqZKGypZkbYfdzF3o4OF95pR/PbKvOJtP+Fi7kYnC4eYSk3n2efDB9sbuK6+dpN/6UaFLu/a+G63i7uaXx6NpBfjbJq6JyoMaaVVQhrEKmw5pvLZVhdX19QXhbmzqTYMGaBhFT1rDzpY+IeL8Z19n+4lKQVM/Npdu5s6sOJf5eZYVR6cm01CrJ6Hu3sfIrbnpIORH+cwslsAHetdvpMtl+WmYsPXk2IgKcbINR/a+f2wSrtivUaHXq2nb7KeY1kq7/7m5PFvHEzta6i0cmDJ+iwmznNP+j714YqZs237QStzV2Wy8KlalV7GLdmU5zE31dRhkRXyd1yFzx93tgsq6iXTsEYYa/8qYOHveYy/KRRVVXlu0RmignXMGxmF2ajwxfo8RsxMZ8GYGGJDK7Z8W7Iui4lz3d1fpo6uUWF/66arQ4v+n1TDTFINM9c8sZ/f/8yjXYOgCvu7S1IKmPiVeyjz1EGhZYT2jRyriwfnZJMQo+fhHu5yzWpXeXpRLs1rG3n9TgtOl8rMX60Mn5PNgofCfPrBp8sp3a//kEdCjDatwz+Ras3m2KSO6MxBWBp0IfLOF3GkHsD656/gdHDq3QFED/kftaYcQnU6sO78ibxtP1CZXR4rq1wrzmpXWbo5nxHXeK8rPHFrKA9fG8yBVCdvLMti8tdnmNg3vMLjJS6dUa/wzk0qT6+Adh+CXtF6DnaqDec2/9+cBO1qaQ2IH22Gcd/CvH4q5gp4HluyKY+Jn2cW/Tz1/iif/41zWW0qSzflMeLakBLrn/4sg+bxJl4fGIzTBTN/zGb4tHQWjI3F4sdpqoQQl7+LbgR85plnGDFiBD/++CNXX60NX9iwYQPLli1j6tSpACxfvpwuXbqUuo8nnniCcePGeawzfdH5ouIREaigVygxWX5ajkp0sPeCLzpYKRk+VyU6yPNtjN2pMnZRAcfOqMwaYPHoBehNcpwehwuOnFGpG1W5hW5EoHZzPHfy3bTcso6Dl+NWLPymwy7Sc6H7u+7hJU4VXl7lYPZGB6seqpwvpBZXlE6v5+8i0xl0Tjrfc/fi1NLpZPYGJ6seMhNTeEwSi51Xk0GhZrjC8ayK70ESEVA83e44pOVSerqDSk4wnZanEh3k3qdB55kmgIRI2FQ4VDq2MGyJMFEKx7PLn56ydGtgIrmmu0iyObS4pOe4iC32xjQtx0WDat4bJyICFfQ6SM85t1xwEX1OD5ScApVhs7MJMin8755gr8Ne/z7l5N6Z2dzR2syIbhU/j5T7evZcr51v79t4Pd9lXBcANcMVIgLgYIZKu9rF/75CRCDUiVRIiFLo+r6dlGMqzeMqp1zr1jSY5DqePXgA0rOcxIa580ZalpMGNcv/QL/pr3zSs510f2J/0TqnC17+Io3ZqzJZ9WKdcu/7fLo1shR9ARCKpTHb5dHolpbtokEpX/iLCNJp+fyc3jFpOS6iC3vRxIZq+T2xiudtPiHWwPEMbbt1f9n4aaeV31+oWvTVzEY1TKzZc5IvN+TxQI+KbYjv1iyY5DrxRT+7z7eD2PDi59txSefbm5oxJiKC9Rw8ZaddA5/u2oNWroUX/exRroWeW655r5KVXq6VvM8XlWtmhf/1D/Eo15ZuLeBohpPPHgxFV/h1mNfuMNDmhdOs3GXjpmTfHePLKd3r99rZc9LJ98+kA6AW7q7dixk82CWA0T0r52sLzux0VKcDfWiMx3p9aCzOM96/BAqAquI4pY2usR3+A2O1JMJuHK81AgK2gykcm9QRJSAURW/ElZNOtadWUXBgS4Wl5VyVVa4V9/3WfKx2ld6tvN+bz86lVreKkbBAhf7vpjPimpAKf7lRlvAA7/f49LzL96Mf53M2Tef2+isrTdGB3uqpnvWcRrEKi++B7AIVu1ObnubO+SqNz/neV4hZIcQM8eGQXFWl3QewYi/clHTJSSuhWyMLyY+6I+Au15zEhhXL5zlOGlQ/Xz737CmYlu0k2kveLMrnrT0P5tLNeRw97eSzR2Lc5fnASNo8dZyV2/O5qcU/NEMJISrFRY/3uf/++/n5558JCgpi0aJFLFq0iMDAQH7++WeGDh0KwPjx45k/f36p+zCbzYSGhnosZuPFRcWkV2hUTcfa/e7KgktVWXfASbM47/tqVkPH2gOelYs1+500q+EOf7YB8OBpFx/1txBxAXOA7T7hQqeUHJJbGUx6hUZVFdYe8JzPb91BV+nHIU7H2oOeN581B9zhezXW89UwE4uHupfYYBjaRs/0O0sOuagMZafT+3FvVl3H2gOeDw9aOrXwvRrr+WqokcX3uRd3OrWbd+Oq2hwk+0+792N3qhw9o1I9rOLPt0mv0KgKrD3kOY/hukMqzaqXlm6FtQfPSfdBd3iTXqFxVdh/zrDeAxlQPVQLExcGscGe6dbCqFSvoA4dwWaF2lH6oiUxVk9MsMLave65gHKsKtuOOGhW0/tDo8mg0Ki6nrX73Nu4XCrr9tk9tsmxqgydlYVRD+8NCMHspQfMXycdDJ6RRe/mZsZeUzmVqaJ8ftBLPq9eyvVc3dv1XHr+ADiRrZKZD7FlNBSe7Q1qu7RRmBcl2KKjdqypaEmsZiImVM/a3e6nhZx8J9v2W2lWt/wvI3q1DeWrZ2qx+Gn3EhuuZ+i1EUwfHeeLpJQq2KKjdrShaEmsYiAmRMfav9wvXXKsLrYdstGstvfy1mRQaFTDyNq/3C8wXC6VdX8V0Ky2VnbFReqJDdWx/5TnsOkDqQ6qF84XlF/49cJzOwwpivv8V6Rgi47aVUxFS2J1EzFhetbuOud877PSLMG3jfAnTtvJzPVsXK4IpZZr+4qXay6tXKtVVrlm8CgLXS6VdXvtNKvlftDMsboY+lHp5Vq+XUWneJ7vsz/7+nxfTul+554QvhwVxuKHteX527SWho/vD6V/20p8qem0YzuYgqVBV/c6RcHSoAsFe3+/8P3odEVzARan5mfhyknHEJuAKb45eSnfXHqcL1BllWvFffF7Ht0aWYgMPn+jXtH9zOHfr6aa9NAwFtYdca9zqbD+MDT13+jtS2LSK1qaDrvXuVSVdYehWSkDvJpV8wwPsPaQ92MQYlaIDFQ4kKmy4xR0r1tGZFStp2BF1VuCLTpqxxiKlsSqhfl8zzn5/KCNZvHnyefFtnHn85LbfLE+12s+z7eVUp5TOfdvIcQ/20XVfu12Ow8++CDPPPMMn376aUXF6YINaWPk8a8LaFxNR3Kcntnr7eTbVfo01SoL//2qgNgQhfHdtUJ1YGsjg+ZambnOTtdEPd/scLDjmIvnCof72p0qjywsYOdxF1PvMuNUVVIL30CHBSiY9ApbjjjZdtRFm3gdQSaFlCMuXlpewC2NDYQF+Kfr9ZCrDTy+1E7jqjqSqyvM3uAk3w59krUbxn+X2LTj0FU7LgNbGRg0z8bM9Q66Jur4ZqeTHcdVnrtBC6/1BvJMi0Gv9TyrG+W/j4QMuVrP40sdNK7mJLmawuyNTvJtxdNpL0ynlq0HttIz6BN7sXS6tHRer/0+IkAh4pxzZtBpbyLPpjPYrHBXcz1TfnVQNVSheijMXK/VLq6vXznHYkgrHY9/66JxFZeW7k0u7fw21uL+32VOYoMpGqI7sIWOQfOdzNzgomtdhW92u9hxAp67xh3foa11jFviolUNF21qKqzer/LjXpU5d2r7UBSFoa11TPnNRVKMiwaxCl/ucLHvNLzdq3LSrSgKg9pbmPqTlfgoPXEROt5ZmU9siI6eDdwVpSEzs+jZ0MSAwoe5IR0sPL4wl8bVDSTXMDB7jVXLJy216/xsA2C+HV69J4icApWcAu06jwxS0OsU9px0MGRmNh0TjQzpYCmaz0qvg8igik3/kFY6Hl/mpHFVJ8nVdFo+t0OfwmHZ//3GoZ3vLmfzuY5BnzqY+buTrgk6vtnlZMcJleeu085lrk0b2nttko7oIIXDmSqv/uSkVgR0rKPloa3HXPxxQqVlnEKoRQvz9q9OaoVD8zIaEyuaoigM6hHO1GWniY818v/t3XdYVOe2BvB36L0oRUSQKipFRcUWGxpsCZYTY8dK1EREBJUUMaAmiorGErsCGqLG7jExFBW7giA2BKUExYYoKk3avn9MGB0HjbmHPZ/uvX7Pw/PInrnnvjsMw561v28tSxN1rDxQCDMjVfRu/XLJwLiIO+jdRg+jexoBAErKa5BX8LJwcOdRJdJvv4ChrgoaN1CHsZ4qjF+7oFZTlcDEQBV2jZR7o0MikcCnmy7WxT+HjYkaLBuqYuUfz2FmoIreLi8LFOPWPkJvV22M/kh63uO66SF4xxO4WKnDzVodUSdKUFbBYcjfW38lEgkm9tTDqj+fw6mxOlpYqmN/UimyH1bhp7HS57Rpqg4DbRUE/1qEr7ykxZPfzpUg/3E1erRU/opviUQCn97GWHe4EDbmGtKf9/5HMDNSQ+9XJj2PW3obvd31MNrTGMDfP++HLwsHdwoqkZ5XDkNdVTRuqI6S8hqsOfQIXu76MDFUw+2CCiz5rQDWZur4yFm5qyUkEgl8umhj3bGyl+9r8aWK72ubn0rf1zpJi5/S97ViuFiqvvK+xr3yvlaDiZHPUVbBYclQ/Trf17o4aGDJkVKEHSzB6E5aqOGAjSfKoKoiQQdbflsdsDxv64byv+tP/t4hYG+qCgNt5V7TPI1dDdOJ6/AiNxUVOckw6P0lJJo6eH56OwDAZOJ6VD25i6K90pY5hv1n4kVuKqoe5kCirgFtVy/odRyOwu0vW/LotBuEmuePUFV4B+pNWqLhiMUoTf0vyq8dVeq5vYqv97Vafz2qQnJ2BTbUse04Mb0cj57XwNVKHTqaEty6X4Ul/30GdxsNNGnAts8tAIxtDXwTDzibSSf/brsElFVJpwUDwNex0puwAZ2l31dUA1mPpf+urAEeFgPpBYCOOtDUiMEJ1GFcG+DrOMDFnIOrORB9Sf6cgmM5mOkCM7tIryfGtAbG7gG2pnDobgP8nglcfQiE9nr5v3nkJocG2tLegJmPgB9PAL3sgC5/tzC5/ZTDH5nSgSLG2sCDYmBTMqCpBnSzUc55SyQS+HTXw7q457AxVYNlAzWs/OOZ9HXu+vLG1bifH6G3qxZGd5X+HRvXQw/BMX+/zptqICqxWPo67/Da67zg79d5HduOuzhpYsmhpwjb8xSju+qipgbYmPAcqipAB4f3t/WBpo4uTJvay743aWKDJi3cUFL0BE/u3X7L/yXhB20bF6t/9ddQXV0de/bswdy5c/nK86/0d1bD41IOqxIrUVBSgRbmKtg4Qku2TeTu0xpIJC8v8NytVLF0kCZWHK/A8mMVsGkgwerPNdHMTPqcB885HM2UFngGbZSfPBk1WgsdbFShoQr8fr0Kq0/UoKIaaGIkwdgO6hjfgV2vsP4tVaX/HU5WoqBE2t9t4+casu2Ad59xcneK3JuoYKm3OlacqMLyxCrYGEuw+j/qaPYeTAF+m/4tVPG4FFh1surleQ5T/4fzVMOKE9VYfqL67/NU+9fnOaunKlRVpEXG8iqgVWMJIkeow1BLOW+c/ZurSM/7dA0KSoEWpsDGz1RfO++XWdwtJVg6QAUrTtVg+SnpFonVg1TQzPTlcz52VMH3HwMbztdg4VFpE+aVA1XQtsnL54xtq4IXVcCi4zV4WgY4mQFbPlOFtZHy/mBM6qqFsgoOIQdK8KycQ1trNWwcK7/SI+9xDZ68Mtynv6smHpdwWJVQhoK/tw5vHKsv2w587W4V0u5If8+9lj+V+/8XH2iIJsaq+PNqBR6XcDiYVoGDaS+LC42NVHA0yIjHM/77dV4GrDpVjYKSaunrfKjaW37eKlj6iRpWnKzC8pN/v84Hv3ydq0qAjAIO+69V4Xk5YKoHdLFRgX9XNVkPUy11IC6zBqtOcSirlD6nq60KpnqrMe9zOqmPsfQ1sP0hnpXWoK2DFjZOt5RbPZ73qFJu0uvVv8oxNiJf9v2i3x4BAAZ10seice/fcotJPfWk57i7CM/KatDWVgMbv2go/zovrMaTkpfn2L+NNh6X1GDVn89R8KwaLSzVsdG3ody2ubHd9PCiksOiA0/xtIyDk4UatkxuCGuTv2+E6Kli4xcNsOL35xi79hGqqgGHRmpYM74Bmr9hKxPfJvVtgLIXHEKi70t/3o7a2DijifzPu6ACT17ZMng1txxjl7788LBol3TwwqDOBlg0wQKqKkDGnRfYf+YZnpdWw9RIDV2cdeE/0AQa/3IXQn2Qva/tL5a+rzVVx8ZxBorva6+0+ejvpin9eSeUoeC5dAvtxnGvvq9VI+3vCeheEUVy///ig4zQxFgVdqaqWDtGH2uOlmH4+mdQkUD2/vjqFl2hnff7pDRpLx7rm8B40DdQNTBHxe0reLD8P6j5e1iIWoMmAPfy75lEUxcNR0dA1bgxuMpyVN7LRMEmX5Qm7ZU9R9WwERoM++HvbcX3UXxmB4oOLVb6ub2Or/c1QDoQpJGhKro0Uyx21N7MWHSgChVVHBoZqcLLVRu+vfQUnstCv2bA4zJg9Xlpq4/mpsB675dbZ+8Vy6/uKigBPtvx8vutqdKv9pZA5BDlZn+Tfs0keFzGYdW5V85poHQoHQDcey5dpVarjYUE4X04rDwLrDgjLWau+gRwfKUFTUEJEH5Suk3YVBcY2ByY8soQbU1V4OJdaRH16Qvpf7+2lkDMUOXuzJrk+ffrfFft61wTGye/9jp/VCV/ndpGB4+La7DqyCuv88kmdbzOS6SvcyfF17mduTrWTmqINX8+x/AVBVBRAVpYamDjZBO5rcnvm6YubRG4PU72/effLAEAnNkbjahgX1axCBEdCcdx/2rR8NixY9G6dWuFwSD/K26be73+730wqpW41+598u9edsJRJdKftxH/TeHfS8+K//k5QmTf9J+fI0TFbxjhLHSG/A/xeS89eMQ6AVGiv/64yjoCE00Ht2UdgYnq3LusIzAh5IGAb6Niy88gsvfdlBmnWUdgYv0r27FFrSabdQL2VN7WY0C4/vW6eEdHR4SFheH06dNo27YtdHXlu9VPnz693sIRQgghhBBCCCGEEEL+d/+6CLh582YYGRnh4sWLuHjxotxjEomEioCEEEIIIYQQQgghhLxn/nURMCcnh48chBBCCCGEEEIIIYRvkvd7HgDhD/3kCSGEEEIIIYQQQggRuH+9EhAA7ty5g4MHDyIvLw8VFRVyj0VERNRLMEIIIYQQQgghhBBCSP3410XAhIQEeHt7w87ODjdu3ICLiwtyc3PBcRzc3UU64ZcQQgghhBBCCCGEkPfYv94O/PXXXyMoKAhXrlyBlpYW9uzZg9u3b6N79+4YOnQoHxkJIYQQQgghhBBCCCH/g39dBExPT4ePjw8AQE1NDWVlZdDT00NYWBgWL15c7wEJIYQQQgghhBBCSH2R0JdI/esioK6urqwPoIWFBbKysmSPPXr0qP6SEUIIIYQQQgghhBBC6sU7FwHDwsJQUlKCjh074tSpUwCA/v37IzAwEAsXLsSECRPQsWNH3oISQgghhBBCCCGEEEL+f965CBgaGoqSkhJERESgQ4cOsmO9evXCzp07YWNjg82bN/MWlBBCCCGEEEIIIYQQ8v/zztOBOY4DANjZ2cmO6erqYt26dfWfihBCCCGEEEIIIYQQUm/euQgIABKJeJsnEkIIIYQQQgghhHz4qLYjVv+qCNisWbN/LAQ+fvz4fwpECCGEEEIIIYQQQgipX/+qCBgaGgpDQ0O+shBCCCGEEEIIIYQQQnjwr4qAw4cPh5mZGV9ZCCGEEEIIIYQQQgghPHjn6cDUD5AQQgghhBBCCCGEkA/Tv54OTAghhBBCCCGEEEI+VLTIS6zeuQhYU1PDZw5CCCGEEEIIIYQQQghP3nk7MCGEEEIIIYQQQggh5MNERUBCCCGEEEIIIYQQQgSOioCEEEIIIYQQQgghhAjcO/cEJIQQQgghhBBCCCEfOAmtBxMr+skTQgghhBBCCCGEECJwVAQkhBBCCCGEEEIIIUTgqAhICCGEEEIIIYQQQojAURGQEEIIIYQQQgghhBCBo8EghBBCCCGEEEIIIaIhYR2AMEIrAQkhhBBCCCGEEEIIETgqAhJCCCGEEEIIIYQQInBUBCSEEEIIIYQQQgghROCoCEgIIYQQQgghhBBCiMBREZAQQgghhBBCCCGEEKHjRK68vJybN28eV15ezjqKUtF503mLAZ03nbcY0HnTeYsBnTedtxjQedN5i4FYz5uQ94WE4ziOdSGSpWfPnsHQ0BBPnz6FgYEB6zhKQ+dN5y0GdN503mJA503nLQZ03nTeYkDnTectBmI9b0LeF7QdmBBCCCGEEEIIIYQQgaMiICGEEEIIIYQQQgghAkdFQEIIIYQQQgghhBBCBE70RUBNTU3MmzcPmpqarKMoFZ03nbcY0HnTeYsBnTedtxjQedN5iwGdN523GIj1vAl5X4h+MAghhBBCCCGEEEIIIUIn+pWAhBBCCCGEEEIIIYQIHRUBCSGEEEIIIYQQQggROCoCEkIIIYQQQgghhBAicFQEJIQQQgghhBBCCCFE4KgISAghhBBCCCGEkHpnZ2eHwsJCheNFRUWws7NjkIgQcRN1EfDWrVv4888/UVZWBgAQ+qDk6upq7N69G/Pnz8f8+fOxe/duVFVVsY5FSL3x9PREUVGRwvFnz57B09NT+YEIIfVGrL/f2dnZrCMQQggh/2+5ubmorq5WOP7ixQvk5+czSESIuEk4oVe+6lBYWIhhw4bh6NGjkEgkuHnzJuzs7DBhwgQYGxtj2bJlrCPWu2vXrsHb2xv379+Hk5MTACAzMxOmpqY4dOgQXFxcGCck9amkpASLFi1CQkICHj58iJqaGrnHhfqhUkVFBffv34eZmZnc8YcPH8LS0hKVlZWMkilPRUUFcnJyYG9vDzU1NdZxCKk3Yv39VlFRQffu3TFx4kR89tln0NLSYh2J8OzmzZs4duxYnX+/Q0JCGKXiX0JCwhuvW7Zs2cIoFRtFRUUwMjJiHYPwrLy8XNDv6QcPHgQADBo0CFFRUTA0NJQ9Vl1djYSEBMTFxSEjI4NVREJESZSfEAMCAqCmpoa8vDy0aNFCdnzYsGGYOXOmIIuAkyZNgrOzM5KTk2FsbAwAePLkCcaNG4cvvvgCZ86cYZywfq1cufKdnzt9+nQek7AxadIkJCYmYsyYMbCwsIBEImEdiVeXL1+W/fv69eu4f/++7Pvq6mocOXIElpaWLKIpTWlpKfz8/BAVFQVAWuS3s7ODn58fLC0tERwczDghP2ovMF8nkUigpaUFBwcH2NraKjmVcojlA7PYf79TUlKwdetWzJw5E9OmTcOwYcMwceJEeHh4sI7GOzHe0Nq4cSOmTp0KExMTNGrUSO7vt0QiEWwRMDQ0FGFhYWjXrp0orltetXjxYtjY2GDYsGEAgM8//xx79uxBo0aN8Pvvv6NVq1aME/IjJSUF6urqcHV1BQAcOHAAW7duRcuWLfH9999DQ0ODcUJ+1NTUYOHChVi3bh0ePHggu16bO3cubGxsMHHiRNYR682gQYMASN+7xo4dK/eYuro6bGxsBPm5m5D3nShXAjZq1Ah//vknWrVqBX19faSlpcHOzg7Z2dlwc3NDcXEx64j1TltbG8nJyXB2dpY7fvXqVbRv3162JVoo3vVDv0QiEeSHCCMjIxw+fBhdunRhHUUpVFRUZB8Y6npL09bWxqpVqzBhwgRlR1Maf39/nD59GitWrEDfvn1x+fJl2NnZ4cCBA/j++++RmprKOiIvan/2r//ca49JJBJ89NFH2L9/v+wGiBD80wfmffv2MUpW/+j3W6qqqgoHDx5EZGQkjhw5gmbNmmHChAkYM2YMTE1NWcfjxYgRI956Q8vf359RMv40bdoUX375JebMmcM6ilJZWFggPDwcY8aMYR1F6WxtbfHLL7+gc+fOiIuLw+eff46dO3di165dyMvLQ2xsLOuIvGjfvj2Cg4Pxn//8B9nZ2XB2dsbgwYORlJSEAQMGYMWKFawj8iIsLAxRUVEICwuDr68vrl69Cjs7O+zcuRMrVqzA2bNnWUesd7a2tkhKSoKJiQnrKIQQAOBESE9Pj8vMzJT9Oysri+M4jktKSuIaNGjAMhpv3NzcuISEBIXjCQkJnIuLC4NEhE82Njbc9evXWcdQmtzcXC4nJ4eTSCRcUlISl5ubK/u6e/cuV1VVxToi76ytrbmzZ89yHCf/vnbz5k1OX1+fZTRexcfHcx06dODi4+O5Z8+ecc+ePePi4+O5Tp06cYcPH+ZOnTrFOTs7cxMmTGAdtV41atSIi46OZh1DKej3W155eTkXERHBaWpqchKJhNPU1OTGjBnD3b17l3W0emdoaMidOnWKdQyl0tfXl71/i0mDBg24W7dusY7BhJaWFpeXl8dxHMdNnz6d++KLLziO47iMjAzOyMiIZTReGRgYyH7mixYt4ry8vDiO47hTp05xTZo0YRmNV/b29lx8fDzHcfLXa+np6YL+edcqKytjHYEQ0RPlYJCuXbsiOjpa9r1EIkFNTQ3Cw8PRs2dPhsnq17Nnz2RfP/74I6ZPn47du3fjzp07uHPnDnbv3o0ZM2Zg8eLFrKOSejZ//nyEhISgtLSUdRSlaNq0KWxsbFBTU4N27dqhadOmsi8LCwuoqqqyjsi7goIChV5pgHQ7nZC3Vfn7+yMiIgK9evWCvr4+9PX10atXLyxZsgSzZs1Cly5dsGLFCsTFxbGOWq8qKirQuXNn1jGUgn6/pZKTk/Hll1/CwsICERERCAoKQlZWFuLi4nD37l0MHDiQdcR6Z2xsjAYNGrCOoVRDhw4V7Mqvt5k0aRJiYmJYx2DC2NgYt2/fBgAcOXIEvXv3BiBd+VzXMAWh4DhOtsU/Pj4e/fv3BwBYWVnh0aNHLKPxKj8/Hw4ODgrHa2pqBNvbtqamBvPnz4elpSX09PRku7Dmzp2LzZs3M05HiPiIsidgeHg4evXqheTkZFRUVGD27Nm4du0aHj9+jNOnT7OOV2+MjIzkPvxzHIfPP/9cYVvVp59+KuiLDAC4c+cODh48iLy8PFRUVMg9FhERwSgVf5YtW4asrCyYm5vDxsYG6urqco+npKQwSlb/3tQTri7e3t48JmGrXbt2OHz4MPz8/ABA9nu+adMmdOrUiWU0XmVlZcHAwEDhuIGBgewi09HRUXAfKGo/MM+dO5d1FKX58ccfYW5urrDtd8uWLSgoKBDs9smIiAhs3boVGRkZ6N+/P6Kjo9G/f3+oqEjv49ra2iIyMhI2NjZsg/Kg9oZWVFQUdHR0WMdRCgcHB8ydOxfnzp2Dq6urwt9vIfYxBqQDEjZs2ID4+Hi4ubkpnLcQr9VqDRkyBCNHjoSjoyMKCwvRr18/AEBqamqdxSKhaNeuHRYsWIDevXsjMTERa9euBQDk5OTA3NyccTr+tGzZEidPnkTTpk3lju/evRtt2rRhlIpfCxYsQFRUFMLDw+Hr6ys77uLighUrVgiqDyIhHwJRFgFdXFyQmZmJ1atXQ19fH8XFxRgyZAi++uorWFhYsI5Xb44dO8Y6wnshISEB3t7esLOzw40bN+Di4oLc3FxwHAd3d3fW8XhR24hXDN71XCUSiaCL3T/88AP69euH69evo6qqCj/99BOuX7+OM2fOIDExkXU83rRt2xazZs1CdHS0rC9aQUEBZs+ejfbt2wOQTtq0srJiGbPeifED8/r16+tcKeTs7Izhw4cLtgi4du1aTJgwAePGjXvjNYqZmZlgVlO0adNG7gbmrVu3RHFDq9aGDRugp6eHxMREhfduiUQi2CLg5cuX0bp1awDSftWvEvJqdgBYvnw5bGxscPv2bYSHh0NPTw8AcO/ePXz55ZeM0/FnxYoVGDVqFPbv349vv/1WVvDcvXu3oFe6h4SEYOzYscjPz0dNTQ327t2LjIwMREdH47///S/reLyIjo7Ghg0b0KtXL0yZMkV2vFWrVrhx4wbDZISIkygHgxBx8fDwQL9+/RAaGiobBGNmZoZRo0ahb9++mDp1KuuIhNSLrKwsLFq0CGlpaSguLoa7uzvmzJkjm7wnRBkZGRg4cCBycnJkhb7bt2/LhqI0a9YM+/fvx/PnzwXVcP5trSskEgmOHj2qxDTKoaWlhfT0dIXBT9nZ2WjZsiXKy8sZJSP1KTQ09J2fO2/ePB6TEKIcJSUl0NXVZR3jvVFeXg5VVVWFor+QnDx5EmFhYXLXayEhIfDy8mIdjRfa2tq4ceMGmjZtKjeU8/r16/Dw8BDkUE5C3meiLAJevny5zuMSiQRaWlqwtraGpqamklPxr6ioCJs3b0Z6ejoA6eqJCRMmwNDQkHEyfunr6+PSpUuwt7eHsbExTp06BWdnZ6SlpWHgwIHIzc1lHZE3Fy9elPt5C3WbARG3mpoaxMbGIjMzEwDg5OSEjz/+WLZdkgiDo6Mj5s2bh9GjR8sd37ZtG+bNmyfISe+vKi0trbOlhZubG6NEhG+1l+hCXwkndnp6evj8888xYcIEfPTRR6zjEFLv2rZti4CAAIwePVquCBgWFoa4uDicPHmSdURCREWU24Fbt26t0Bfv1QssdXV1DBs2DOvXr4eWlhaTjPUtOTkZffr0gba2Njw8PABIt4stXLgQsbGxgt0WCwC6urqyD00WFhbIysqCs7MzAAiuT1ithw8fYvjw4Th+/DiMjIwASIvAPXv2xI4dO2TbJoUmLCzsrY+HhIQoKYny/f7771BVVUWfPn3kjv/555+oqamR9RgSIhUVFfTt2xd9+/ZlHYXwyNfXFzNmzEBlZSU8PT0BSNs9zJ49G4GBgYzT8aegoADjxo3DkSNH6nxcyG0O7OzskJSUhIYNG8odLyoqgru7u2ALv9HR0ViyZAlu3rwJAGjWrBlmzZolqNXMrxs8eHCdxc7aG/QODg4YOXIknJycGKTj1/bt2xEZGQlPT0/Y2NhgwoQJ8PHxQePGjVlH45WKispbC9xCfm+rVVxcLBuOUquuPscfOjFugSbkfSbKlYAHDhzAnDlzMGvWLFlB7MKFC1i2bBnmzZuHqqoqBAcHY9iwYVi6dCnjtPWja9eucHBwwMaNG6GmJq39VlVVYdKkScjOzsaJEycYJ+TPoEGDMGDAAPj6+iIoKAgHDhzAuHHjsHfvXhgbGyM+Pp51xHo3bNgwZGdnIzo6Gi1atAAAXL9+HWPHjoWDgwN+/fVXxgn58fpKx8rKSuTk5EBNTQ329vaC7B9Vy83NDYsWLZJN16t15MgRzJkzB2lpaYyS8S8hIQEJCQl4+PChwsX0li1bGKXiX3JyMnbt2lXn6rC9e/cySsUfjuMQHByMlStXys5XS0sLc+bMEXSBf9SoUfjrr7+wYsUK9OjRA/v27cODBw+wYMECLFu2DAMGDGAdkTcqKiq4f/++wuTzBw8ewMrKSuF1LwQRERGYO3cupk2bhi5dugAATp06hTVr1mDBggUICAhgnJAf48aNw/79+2FkZIS2bdsCkPZ8LCoqgpeXF9LS0pCbm4uEhATZfxehKSgowLZt2xAZGYn09HT06dMHEyZMgLe3t+zaXUgOHDgg931lZSVSU1MRFRWF0NBQwQ6LyMnJwbRp03D8+HG5NhYcxwm6f7XYtkAT8j4TZRHQw8MD8+fPr3PFzNy5c3HhwgXs378fgYGByMrKYpSyfmlrayM1NRXNmzeXO379+nW0a9cOpaWljJLxLzs7G8XFxXBzc0NJSQkCAwNx5swZODo6IiIiQmE6lxAYGhoiPj5eNhih1oULF+Dl5YWioiI2wRh49uwZxo0bh8GDBwt6FYW2tjbS09MVJoTm5ubC2dkZJSUlbILxLDQ0FGFhYWjXrh0sLCwUVhXs27ePUTJ+7dixAz4+PujTpw9iY2Ph5eWFzMxMPHjwAIMHD8bWrVtZR+RNcXEx0tPToa2tDUdHR0G273iVhYUFDhw4AA8PDxgYGCA5ORnNmjXDwYMHER4ejlOnTrGOWO9qp74PGjQIUVFRcm1LqqurkZCQgLi4OGRkZLCKyBtbW1uEhobCx8dH7nhUVBS+//575OTkMErGr+DgYDx79gyrV6+WtXKoqamBv78/9PX1sXDhQkyZMgXXrl0T5Gv+datWrcKsWbNQUVEBExMTTJkyBcHBwaKYkh0TE4OdO3cqFAmFokuXLuA4Dv7+/jA3N1e4bunevTujZIQQ0eBESEtLi0tPT1c4np6ezmlpaXEcx3E5OTmctra2sqPxxszMjPvzzz8Vjh85coQzMzNjkIjwSU9Pj0tNTVU4npKSwunr6ys/EGOXL1/mmjZtyjoGr8zNzbmEhASF43FxcZypqSmDRMrRqFEjLjo6mnUMpXN1deVWr17NcZz09z0rK4urqanhfH19uZCQEMbpSH3S19fncnJyOI7jOGtra+7UqVMcx3Fcdna2oK5TXiWRSDiJRMKpqKjI/l37paGhwTVr1ow7dOgQ65i80NTU5G7evKlwPDMzk9PU1GSQSDlMTEy4jIwMheMZGRlcw4YNOY6T/i03NDRUcjLluX//Prd48WKuRYsWnI6ODjdq1Cju6NGjXHR0NOfs7Mx9/PHHrCMqRVZWFqerq8s6Bm90dXW5GzdusI5BCBExUXZNb968ORYtWiS3jaSyshKLFi2SrZTLz8+Hubk5q4j1btiwYZg4cSJ27tyJ27dv4/bt29ixYwcmTZqEESNGsI7HKzs7OxQWFiocLyoqgp2dHYNE/PP09IS/vz/u3r0rO5afn4+AgAD06tWLYTI2nj59iqdPn7KOwauBAwdixowZcquXb926hcDAQHh7ezNMxq+Kigp07tyZdQyly8rKkm0D1dDQQElJCSQSCQICArBhwwbG6fiTnJyM2bNnY/jw4RgyZIjcl1A5OTnJVry1atUK69evR35+PtatWwcLCwvG6fhRU1ODmpoaWFtby7b51369ePECGRkZ+OSTT1jH5IWDgwN27dqlcHznzp1wdHRkkEg5qqqqcOPGDYXjN27ckG2P1NLSEuSQlL179+LTTz+FlZUVYmJi8OWXXyI/Px/bt29Hz549MWbMGBw4cADHjx9nHZV3ZWVlWLlyJSwtLVlH4U379u1x+/Zt1jGUytjYGA0aNFD4atiwISwtLdG9e3dB72Ag5H0jvAYT72DNmjXw9vZGkyZNZFP1rly5gurqallz0uzsbHz55ZcsY9arpUuXQiKRwMfHB1VVVeA4DhoaGpg6dSoWLVrEOh6vcnNz6+yv8eLFC+Tn5zNIxL/Vq1fD29sbNjY2sLKyAgDcvn0bLi4u2L59O+N0/Fm5cqXc9xzH4d69e9i2bZugB2MAQHh4OPr27YvmzZujSZMmAIA7d+6ga9eugultWpdJkyYhJiYGc+fOZR1FqYyNjfH8+XMAgKWlJa5evQpXV1cUFRUJtr3DP22BFip/f3/cu3cPADBv3jz07dsX27dvh4aGBqKiohin45dQt76+TWhoKIYNG4YTJ07Iet+dPn0aCQkJdRYHhWLMmDGYOHEivvnmG1krk6SkJPzwww+yrdGJiYmywW5CMn78eAwfPhynT59WaONSq3Hjxvj222+VnIxfxsbGckVdjuPw/Plz6OjoCPpaddOmTZgyZQry8/Ph4uICdXV1uceFOPE9JCQECxcuRL9+/eT68R85cgRfffUVcnJyMHXqVFRVVcHX15dxWkKET5Q9AQHg+fPn+OWXX5CZmQlAeqd95MiR0NfXZ5yMX6WlpbKVQvb29oLuLSLmnkKA9GIqPj5edme9RYsW6N27N+NU/LK1tZX7XkVFBaampvD09MTXX38t+N9vjuMQFxeHtLQ0aGtrw83NDd26dWMdi1f+/v6Ijo6Gm5sb3NzcFC6mIyIiGCXj18iRI9GuXTvMnDkT8+fPx6pVqzBw4EDExcXB3d1dkINB3NzcMHnyZHz11VfQ19dHWloabG1tMXnyZFhYWCA0NJR1RKUoLS3FjRs3YG1tDRMTE9ZxeCfGwT8XL17E8uXLkZ6eDkD69zswMFBh+JWQVFdXY9GiRVi9ejUePHgAADA3N4efnx/mzJkDVVVV5OXlQUVFRXajSyhKS0sFfT3+Jq/fxKi9ZuvQoQOMjY0ZpeLfuXPnMHLkSOTm5sqOSSQSQQ8G+c9//oOPP/4YU6ZMkTu+fv16xMbGYs+ePVi1ahU2bNiAK1euMEpJiHiItggISIdi1DVVUShb54YMGYLIyEgYGBj841YpPT09ODs7Y8qUKXLFsg9ZbWPp2j+sr1JXV4eNjQ2WLVsm2C1FhIhBz5493/iYRCLB0aNHlZhGeR4/fozy8nI0btwYNTU1CA8Plw08+u677wT5AUpXVxfXrl2DjY0NGjZsiOPHj8PV1RXp6enw9PSUrZYTgpkzZ77zc4Va6AbEO/hH7J49ewYAMDAwYJxE+crLyxU+l4jxv4OQtWzZEi1atMDs2bPrHAwixIGFenp6uHTpEhwcHOSO37p1C61bt0ZxcTGysrJkQxwJIfwS5Xbg7OxsDB48GFeuXJG781JLKHdgDA0NZef1T4W9Fy9eYN26dTh9+rRsBd2HrnbFgK2tLZKSkgS/YmLlypX44osvoKWlpbAt9nXTp09XUiqiTGJcMXPs2DHWEZho0KCB7N8qKioIDg5mmEY5xLQFOjU1Ve77lJQUVFVVwcnJCQCQmZkJVVVVtG3blkU8pVm3bh0iIyMFPdkdkBa9ags9tQWwNxFDQUgM5/iqkpISzJkzB7t27aqzh7VQPpfUpaioCJs3b5atenV2dsaECRMEsyChLn/99RcOHjyoUBATsgYNGuDQoUMICAiQO37o0CHZ9UxJSYngd+wQ8r4QZRHQ398ftra2SEhIgK2tLc6fP4/Hjx8jMDBQUL2zXm2w+i7NVq9fv/7GXiQfMrH0FFq+fDlGjRoFLS0tLF++/I3Pk0gkgi4CJicnY9euXXWu8hXiFsla/7RihghPdXU19u/fL/fhydvbG6qqqoyT8aNbt26Ii4uDq6srhg4dCn9/fxw9ehRxcXGCG3j0anE7IiIC+vr6iIqKkq3wfPLkCcaPH4+uXbuyiqgUYhn8Y2xsjHv37sHMzAxGRkZ1vn8Lcaugu7s7EhISYGxsjDZt2rz171ZKSooSkynX7NmzcezYMaxduxZjxozBmjVrkJ+fj/Xr1wu6b3dycjL69OkDbW1tWZ+4iIgILFy4ELGxsXB3d2eckB+enp5IS0sTVRFw7ty5mDp1Ko4dOyb7WSclJeH333/HunXrAABxcXHo3r07y5iEiIYotwObmJjg6NGjcHNzg6GhIS5cuAAnJyccPXoUgYGBCnfgxaK6uhpXr15Fq1atWEf5n9GqOHH6p8EBQp48ZmFhgfDwcMGvmAH+XasDoRZ+b926hQEDBuDOnTuy1WEZGRmwsrLC4cOHYW9vzzhh/RPjFmhAuuoxNjZWYSDC1atX4eXlJTcFXmjmzJkDPT09wQ/+SUxMRJcuXaCmpobExMS3PldIH5JDQ0Mxa9Ys6Ojo/GNPz3nz5ikplfJZW1sjOjoaPXr0gIGBAVJSUuDg4IBt27bh119/xe+//846Ii+6du0KBwcHbNy4EWpq0nUpVVVVmDRpErKzs3HixAnGCfmxYcMGLFiwABMmTICrq6tCL2OhtKV63enTp7F69WpZL3YnJyf4+fmJ4kYPIe8bURYBjY2NkZKSAltbW9jb22PTpk3o2bMnsrKy4OrqKrhtRWJka2uL5ORkNGzYEDY2Nm+8uyyRSJCdna3kdPwLCwtDUFCQQqPpsrIyLFmyBCEhIYyS8UvMgwMaNmyICxcuCLL487rx48dj5cqV0NfXx/jx49/6XKEWfvv37w+O4/DLL7/IttIUFhZi9OjRUFFRweHDhxknJPVFX18fhw4dQo8ePeSOHzt2DN7e3rIt0kIkxsE/eXl5sLKyUrhu4TgOt2/fhrW1NaNkhC96enq4fv06rK2t0aRJE+zduxceHh7IycmBq6sriouLWUfkhba2NlJTU9G8eXO549evX0e7du0E+3mstmd5XYS22hcAKisrMXnyZMydO1dhgB8hhA1RFgG7du2KwMBADBo0CCNHjsSTJ0/w3XffYcOGDbh48SKuXr3KOiIh/xNVVVXZ1qJXFRYWwszMTHAXGLXENDjgdWJZMUOkdHV1ce7cObi6usodT0tLQ5cuXQT5oVGs72s+Pj44efIkli1bJttGdf78ecyaNQtdu3ZVmLApJGIc/CPW17mYubm5YdWqVejevTt69+6N1q1bY+nSpVi5ciXCw8Nx584d1hF5YW5ujm3btsHLy0vu+J9//gkfHx/ZlGjy4TM0NMSlS5eoCEjIe0KUPQG/++472eShsLAwfPLJJ+jatSsaNmyInTt3Mk5H6lNlZSWaN2+O//73v2jRogXrOErz+rCbWmlpaXIDBYRGTIMDXldeXo4NGzYgPj5eNCtmAOnqVo7jZKte//rrL+zbtw8tW7ZU+GAhJJqamnWuACsuLoaGhgaDRPx70z3LFy9eCPacAelwjKCgIIwcORKVlZUAADU1NUycOBFLlixhnI5fYhz886a/38XFxdDS0mKQSDmqq6uxfPnyN/b0ffz4MaNk/Bs/fjzS0tLQvXt3BAcH49NPP8Xq1atRWVkp2L/dADBs2DBMnDgRS5culW0JPX36NGbNmoURI0YwTkfq06BBg7B//36FwSCEEDZEWQTs06eP7N8ODg64ceMGHj9+DGNjY2qmLzDq6uooLy9nHUNpal/DEokEzZo1U5h6XVxcjClTpjBMyC8xDQ543eXLl9G6dWsAUFjNLOT3tYEDB2LIkCGYMmUKioqK4OHhAQ0NDTx69AgRERGYOnUq64i8+OSTT/DFF19g8+bNcqvDpkyZIrh+QrV9XSUSCTZt2gQ9PT3ZY9XV1Thx4oTCdjIh0dHRwc8//4wlS5YgKysLAGBvbw9dXV3GyZSrdjVUkyZNGCfhx8yZMwFIX+dz586Va+dRXV2N8+fPy97jhSg0NBSbNm1CYGAgvvvuO3z77bfIzc3F/v37BdvCpNarhZHevXvjxo0buHjxIhwcHODm5sYwGb+WLl0KiUQCHx8fVFVVAZBet0+dOlXQA1EAaR/QpUuXygZ7tWzZUra6W4gcHR0RFhaG06dPo23btgp/v6g/OyHKJcrtwERcfvjhB2RmZmLTpk2yxsNCFRUVBY7jMGHCBKxYsQKGhoayxzQ0NGBjY4NOnToxTMgvsQ4OEDMTExMkJibC2dkZmzZtwqpVq5Camoo9e/YgJCREdoEtNEVFRRg7diwOHTokW/VZWVmJgQMHYuvWrTAyMmIbsB7Vbh/666+/0KRJE7npx7Xva2FhYejQoQOriIQnNTU1WLBgAZYtWybb4q6vr4/AwEB8++23b+2t9aGp3fqcmJiITp06ya1urX2dBwUFwdHRkVVEXtnb22PlypUYMGAA9PX1cenSJdmxc+fOISYmhnVEwpPS0lK5Gxyv97MWmu3bt2P8+PEYMmQIunTpAkC6AnLfvn2IjIzEyJEjGSesf2/bBizU/uyEvM+oCEgEb/DgwUhISICenh5cXV0V7j4JcXpoYmIiOnfurLAlVIhmzpyJ+fPnQ1dXFydOnEDnzp0FX+wlL+no6ODGjRuwtrbG559/DmdnZ8ybNw+3b9+Gk5OT4LeB37p1S1bobNGiBRwcHBgn4k/Pnj2xd+9eKuaLyNdff43NmzcjNDRU9mH51KlT+P777+Hr64uFCxcyTlj/xo8fj59++gkGBgasoyiVrq4u0tPTYW1tDQsLCxw+fBju7u7Izs5GmzZt8PTpU9YR61XtCud3QaukhKVFixb44osvFLbGRkREYOPGjYK9eUkIeX9QEZAInlinh9YqLy9X6K0jpA8X6urquHPnDszNzd/YUF0skpOT39hPSYjFbkDaUH3SpEkYPHgwXFxccOTIEXTq1AkXL17EgAEDcP/+fdYR603tdsF3IeQ+UkQ8GjdujHXr1ilscT9w4AC+/PJL5OfnM0pG6puTkxOio6PRoUMHfPTRR/jkk08QHByMnTt3ws/PDw8fPmQdsV69vjKqoKAApaWlslXcRUVF0NHRgZmZmaBWSQ0ZMgSRkZEwMDDAkCFD3vpcoV63aGpq4tq1awo37W7dugUXFxdRtTEihLBBy2WI4Am9yFeX0tJSzJ49G7t27UJhYaHC40KaLmhjY4OVK1fCy8sLHMfh7Nmzb1wp1K1bNyWnU54dO3bAx8cHffr0QWxsLLy8vJCZmYkHDx5g8ODBrOPxJiQkBCNHjkRAQAB69eol2+4eGxuLNm3aME5Xv1JTU9/peULtAVldXY3IyEgkJCTg4cOHqKmpkXtciJNixe7x48d19nts3ry5oAdFiPGGTu2ujQ4dOsDPzw+jR4/G5s2bkZeXJ8hhAjk5ObJ/x8TE4Oeff8bmzZvh5OQEAMjIyICvry8mT57MKiIvDA0NZX+jXm1ZIyZWVlZISEhQKALGx8fDysqKUSr+3blzBwcPHqzzfY1uXBKiXLQSkBAB+uqrr3Ds2DHMnz8fY8aMwZo1a5Cfn4/169dj0aJFGDVqFOuI9Wb//v2YMmUKHj58CIlE8sYJohKJRFDFz9e5ublh8uTJ+Oqrr6Cvr4+0tDTY2tpi8uTJsLCwQGhoKOuIvLl//z7u3buHVq1ayXqEXbhwAQYGBoIeGCE206ZNQ2RkJAYMGAALCwuFYufy5csZJSN86dChAzp06KCwddLPzw9JSUk4d+4co2T8+acbOmK5sXn27FmcPXsWjo6O+PTTT1nH4ZW9vT12796tcOPq4sWL+Oyzz+QKhuTDt3btWsyYMQMTJkyQm4ocGRmJn376SXCFXwBISEiAt7c37OzscOPGDbi4uCA3Nxccx8Hd3Z1u4hGiZFQEJKKwe/fuN95VT0lJYZSKP9bW1oiOjkaPHj1gYGCAlJQUODg4YNu2bfj111/x+++/s45Y74qLi2FgYICMjIw3bgcW8l1nXV1dXLt2DTY2NmjYsCGOHz8OV1dXpKenw9PTE/fu3WMdkZD/iYmJCaKjo9G/f3/WUYiSJCYmYsCAAbC2tpat8j179izy8vLwxx9/CHKSpphv6IiVjo4OEhMT0b59e7njFy5cQI8ePQTb23bBggUYNWrUW4dGCNW+ffuwbNkyuZ6+s2bNwsCBAxkn44eHhwf69euH0NBQ2fuamZkZRo0ahb59+2Lq1KmsIxIiKsIZq0bIG6xcuRLjx4+Hubk5UlNT4eHhgYYNGyI7Oxv9+vVjHY8Xjx8/hp2dHQBp/7/abVMfffQRTpw4wTIab/T09HDs2DHY2trC0NCwzi8hMzY2xvPnzwEAlpaWuHr1KgBpXyGhfoAApMMiPD093/hFhENDQ0PQg0+Iou7duyMjIwNDhgxBUVERioqKMGTIEGRmZgqyAAgAWVlZGDBgAADpa76kpAQSiQQBAQHYsGED43T8ysjIwLRp09CrVy/06tUL06ZNQ0ZGButYvOvVqxcmT54sd1P64sWLmDp1Knr37s0wGb9+++03ODg4oHPnzvj555/x6NEj1pGUZvDgwTh16hQKCwtRWFiIU6dOCbYACADp6enw8fEBAKipqaGsrAx6enoICwvD4sWLGacjRHyoCEgE7+eff8aGDRuwatUqaGhoYPbs2YiLi8P06dMFN22ulp2dnWz7SPPmzbFr1y4AwKFDh2RNp4Woe/fu+Ouvv/Ddd99hxIgRskbif/zxB65du8Y4Hb+6deuGuLg4AMDQoUPh7+8PX19fjBgxAr169WKcjj+tW7dGq1atZF8tW7ZERUUFUlJS4OrqyjoeqUeBgYH46aef3rjlnwhTw4YN4e3tjfHjx2Ps2LHw8PBAcnIyDh48yDoaL8R6Q2fPnj1wcXHBxYsXZe/nKSkpcHFxwZ49e1jH49WWLVvQqFEjtGvXDpqamtDU1ET79u1hbm6OTZs2sY7Hm7S0NFy+fBk9evTA0qVL0bhxYwwYMAAxMTGCfq0nJSXh/PnzCsfPnz+P5ORkBon4p6urK9uJZWFhgaysLNljYir+EvK+oO3ARPB0dHSQnp6Opk2bwszMDHFxcWjVqhVu3ryJjh071jk440O3fPlyqKqqYvr06YiPj8enn34KjuNQWVmJiIgI+Pv7s47Ii8TERPTr1w9dunTBiRMnkJ6eDjs7OyxatAjJycnYvXs364i8efz4McrLy9G4cWPU1NQgPDwcZ86cgaOjI7777rs3DksRqu+//x7FxcVYunQp6yikngwePBjHjh1DgwYN4OzsDHV1dbnHhTowQcyOHDkCHx8fFBYWKhR/hdrndeTIkWjXrh1mzpyJ+fPnY9WqVRg4cCDi4uLg7u4u2Ne5vb09Ro0ahbCwMLnj8+bNw/bt2+WKBkJ18+ZN2fbQ5s2bo1mzZowTKdfp06cRExOD3377DeXl5Xj27BnrSLzw8PDA7Nmz8dlnn8kd37t3LxYvXlxngfBDFRYWhsDAQIwaNQoDBgyAr68vgoKCcODAAYwbNw579+6FsbEx4uPjWUclRFSoCEgEz87ODnv27EGbNm3Qrl072bS12NhYDB8+XNATBmv99ddfuHjxIhwcHODm5sY6Dm86deqEoUOHYubMmbKeI3Z2drhw4QKGDBmCO3fusI5IlOTWrVvw8PAQxe+3WIwfP/6tj4tlYIKYODo6wsvLCyEhITA3N2cdRynEekNHR0cHly9fVtjyf/PmTbRq1UrQK8MAYPPmzVi+fDlu3rwJQPranzFjBiZNmsQ4mfJcunQJ27dvx44dO1BYWIiysjLWkXihp6eHy5cvy9r21MrJyYGbm5tsJbAQqKqq4t69eyguLkZxcTHc3NxQUlKCwMBA2ftaREQEmjZtyjoqIaKixjoAIXzz9PTEwYMH0aZNG4wfPx4BAQHYvXs3kpOTMWTIENbxlKJp06ai+AN75coVxMTEKBw3MzMT5HaDf3OX3MDAgMck75+zZ89CS0uLdQxSj6jIJz4PHjzAzJkzRVMArKqqwn//+1/06dMHAKCiooLg4GDGqZSjR48eOHnypEIR8NSpU4Lt/1grJCQEERER8PPzkxuAExAQgLy8PIXVkUKSk5ODmJgYxMTEICMjA927d0doaKjCKjkh0dTUxIMHDxSKgPfu3YOamrA+mteuNXr1XHV1dbFu3TpWkQghoCIgEYENGzagpqYGAPDVV1/BxMQEp0+fhre3N6ZMmcI4HT+mT58OBwcHTJ8+Xe746tWrcevWLaxYsYJNMJ4ZGRnh3r17CpPmUlNTYWlpySgVf4yMjCCRSN76HI7jBLttDoBCIZ/jONy7dw/JycmYO3cuo1SETwUFBbJhAU5OTjA1NWWciPDls88+w/Hjx2Fvb886ilKoqalhypQpsi2hYuLt7Y05c+bg4sWL6NixIwDg3Llz+O233xAaGirXA9Lb25tVTF6sXbsWGzduxIgRI2THvL294ebmBj8/P8EWATt27IikpCS4ublh/PjxGDFihCCv1V7n5eWFr7/+GgcOHJANrSsqKsI333yDjz/+mHG6+vdP16mEEOWj7cBEFMrLy3H58mU8fPhQVhAEpH+YPv30U4bJ+GFpaYmDBw+ibdu2csdTUlLg7e0t2G2xQUFBOH/+PH777Tc0a9YMKSkpePDgAXx8fODj44N58+axjlivEhMT3/m53bt35zEJO69vEVVRUYGpqSk8PT3h5eXFKBXhQ0lJCfz8/BAdHS17H1dVVYWPjw9WrVoFHR0dxglJfSstLcXQoUNhamoKV1dXhT6Qr9/oEoIePXogICBA0JNC66Ki8m6zCoV4U8vIyAhJSUlwdHSUO56ZmQkPDw8UFRWxCcazb7/9FqNGjULLli1ZR1Gq/Px8dOvWDYWFhWjTpg0A6VZoc3NzxMXFwcrKinHC+qOiogJDQ8N/LARS6xZClIuKgETwjhw5gjFjxtQ5AESIF5MAoKWlhatXrypsq7l16xZcXFxQXl7OKBm/Kioq8NVXXyEyMhLV1dVQU1NDVVUVRo0ahcjISKiqqrKOSAj5f5o8eTLi4+OxevVqdOnSBYB0q+D06dPx8ccfY+3atYwTkvq2efNmTJkyBVpaWmjYsKHcB0mJRILs7GyG6fixa9cufP311wgICEDbtm2hq6sr97iQ+/qKlZ+fH9TV1RERESF3PCgoCGVlZVizZg2jZMpRUVGBnJwc2NvbC2477JuUlJTgl19+QVpaGrS1teHm5oYRI0Yo3Oj40KmoqGDFihWyFY9vMnbsWCUlIoQAVAQkIiDGxuIuLi6YMmUKpk2bJnd81apVWLt2La5fv84omXLcvn0bV65cQXFxMdq0aaNwd13ISktLkZeXh4qKCrnjQv/gePHiRdkWOmdnZ9nddSIcJiYm2L17N3r06CF3/NixY/j8889RUFDAJhjhTaNGjTB9+nQEBwe/80qxD11d5ymRSATf2kHMalc4W1lZybZCnz9/Hnl5efDx8ZErDL1eKPyQlZWVYdq0aYiKigIgXfloZ2cHPz8/WFpaCrYf5okTJ9C5c2eFgmdVVRXOnDmDbt26MUpW/1RUVHD//n2YmZmxjkIIeYU4brcQURNbY3EAmDlzJqZNm4aCggJ4enoCABISErBs2TLB9QOcOXPmWx8/d+6c7N9Cunh+XUFBAcaPH48//vijzseF+sHx4cOHGD58OI4fPw4jIyMA0t46PXv2xI4dO6hfnICUlpbW+T5uZmYm+MmhYlVRUYFhw4aJpgAISAcliFVSUhKOHTum0LoFEPbf76tXr8Ld3R0AkJWVBUB608PExARXr16VPU9ovdWCg4ORlpaG48ePo2/fvrLjvXv3xvfffy/YImDPnj1x7949hcLY06dP0bNnT0FdrwntNUuIUFARkAie2BqLA8CECRPw4sULLFy4EPPnzwcA2NjYYO3atfDx8WGcrn6lpqbKfZ+SkoKqqio4OTkBkN5ZVlVVVeiPKDQzZsxAUVERzp8/jx49emDfvn148OABFixYgGXLlrGOxxs/Pz88f/4c165dQ4sWLQAA169fx9ixYzF9+nT8+uuvjBOS+tKpUyfMmzcP0dHRssnPZWVlCA0NlU3UJMIyduxY7Ny5E9988w3rKErTtGlT1hGY+OGHH/Ddd9/ByckJ5ubmClu/hezYsWOsIzCxf/9+7Ny5Ex07dpT7GTs7O8uKoUJUu6r3dYWFhQrb/z90tOGQkPcTbQcmgifGxuKvKigogLa2NvT09FhH4V1ERASOHz+OqKgoGBsbAwCePHmC8ePHo2vXrggMDGSckD8WFhY4cOAAPDw8YGBggOTkZDRr1gwHDx5EeHg4Tp06xToiLwwNDREfH4/27dvLHb9w4QK8vLwE21BdjK5cuYK+ffvixYsXaNWqFQAgLS0NmpqaiI2NhbOzM+OEpL5Nnz4d0dHRaNWqFdzc3BT+fgtxdVh0dPRbHxfajbxa5ubmWLx4McaNG8c6ClESHR0dXL16FXZ2dtDX10daWhrs7OyQlpaGbt264enTp6wj1qshQ4YAAA4cOIC+fftCU1NT9lh1dTUuX74MJycnHDlyhFVEQohI0EpAIni//vorYmNjoaWlhePHjyvcXRZ6EVBM2yGXLVuG2NhYWQEQAIyNjbFgwQJ4eXkJughYUlIi21pibGyMgoICNGvWDK6urkhJSWGcjj81NTV1NtJWV1dX2E5GPmyurq64efMmfvnlF9y4cQMAMGLECIwaNQra2tqM0xE+XLlyRdbf89VtkYBwV4f5+/vLfV9ZWYnS0lJoaGhAR0dHsEVAFRUV2cAfIg7t2rXD4cOH4efnB+Dl7/SmTZsEubq7djgGx3HQ19eX+7uloaGBjh07wtfXl1U8QoiIUBGQCN63336L0NBQUTUWt7W1fesHJCFOVASAZ8+e1TkcoKCgAM+fP2eQSHmcnJyQkZEBGxsbtGrVCuvXr4eNjQ3WrVsHCwsL1vF44+npCX9/f/z6669o3LgxACA/Px8BAQHo1asX43SkPv34448wNzdX+JC0ZcsWFBQUYM6cOYySEb6IcZvkkydPFI7dvHkTU6dOxaxZsxgkUo6AgACsWbNGcH2LyZv98MMP6NevH65fv46qqir89NNPuH79Os6cOYPExETW8erd1q1bAUjb8wQFBQlu6y8h5MNB24GJ4DVo0ABJSUmi6gn4008/yX1fWVmJ1NRUHDlyBLNmzRJss2UfHx+cPHkSy5Ytg4eHBwDphL1Zs2aha9eusgl0QrR9+3ZUVVVh3LhxuHjxIvr27YvCwkJoaGggKioKw4YNYx2RF7dv34a3tzeuXbsGKysrAEBeXh5cXV1x8OBBNGnShHFCUl9sbGwQExODzp07yx0/f/48hg8fLuqBCkT4kpOTMXr0aNkqWKGpqanBgAEDkJmZiZYtWyqs8N67dy+jZIRP2dnZ+PHHH5GWlobi4mK4u7tjzpw5cHV1ZR2NEEIEi4qARPACAgJgamoqqsbib7JmzRokJyfL7kYKTWlpKYKCgrBlyxZUVlYCANTU1DBx4kQsWbJENHddOY5DWVkZbty4AWtra5iYmLCOxCuO45CQkID09HQAQIsWLdC7d2/GqUh909LSQnp6OmxtbeWOZ2dno2XLligvL2eUjBD+Xbp0Cd26dcOzZ89YR+HFtGnTsGnTJvTs2VNhMAgAwV63iFVlZSUmT56MuXPnKrynC51Yd+sQQt4fVAQkgifGxuJvkp2djdatWwv2Q0StkpIS2WQ5e3t70RT/Nm/ejOXLl+PmzZsAAEdHR8yYMQOTJk1inIxfCQkJSEhIwMOHDxX6AG7ZsoVRKlLfHB0dMW/ePIwePVru+LZt2zBv3jz64EQE4eDBg3LfcxyHe/fuYfXq1bCyssIff/zBKBm/9PX1sWPHDgwYMIB1FKIkhoaGuHTpkuiKgGLdrUMIeX9QT0AieGJsLP4mu3fvRoMGDVjH4J2uri7c3NxYx1CqkJAQREREwM/PT9ZQ++zZswgICEBeXh7CwsIYJ+RHaGgowsLC0K5dO1hYWIjud1pMfH19MWPGDFRWVsLT0xOAtAA8e/ZsQQ/9IeIyaNAgue8lEglMTU3h6emJZcuWsQmlBA0aNBBV2xYifa3v378fAQEBrKMo1evDf2rV7tYhhBC+0UpAQgSoTZs2csUQjuNw//59FBQU4Oeff8YXX3zBMB3hg6mpKVauXIkRI0bIHf/111/h5+eHR48eMUrGLwsLC4SHh2PMmDGsoxCecRyH4OBgrFy5EhUVFQCkW4TnzJmDkJAQxukIIf+LrVu34siRI9i6dSt0dHRYxyFKsGDBAixbtgy9evVC27ZtFXZtTJ8+nVEyNsSyW4cQwh4VAQkRoNDQULnvVVRUYGpqih49eqB58+aMUhE+GRkZISkpCY6OjnLHMzMz4eHhgaKiIjbBeNawYUNcuHCBVpCISHFxMdLT06GtrQ1HR0doamqyjkTI/2TmzJnv/FyhtjBp06YNsrKywHEcbGxsFFq3pKSkMEpG+PK2bcASiUR0LR7Cw8Px888/Izc3l3UUQojA0XZgQgRo3rx5rCMQJRszZgzWrl2r8AFxw4YNGDVqFKNU/Js0aRJiYmIwd+5c1lGIkujp6aF9+/asYxBSb1JTU+W+T0lJQVVVFZycnABIb+aoqqqibdu2LOIpxevboInwvTrRvXZNihhaevzTbh1CCOEbFQEJEYh/s33AwMCAxyREWV5dPSKRSLBp0ybExsaiY8eOAIDz588jLy8PPj4+rCLy4tXzrqmpwYYNGxAfHy/6wT+EkA/TsWPHZP+OiIiAvr4+oqKiYGxsDAB48uQJxo8fj65du7KKyDu6eSlOYhxo9nrBm3brEEKUjbYDEyIQKioq73wHtbq6muc0RBl69uz5Ts+TSCQ4evQoz2mUR6znTQgRPktLS8TGxsLZ2Vnu+NWrV+Hl5YW7d+8ySkZI/XrTQLPVq1cjICBAsAPNCCGENSoCEiIQiYmJsn/n5uYiODgY48aNk7uwioqKwo8//oixY8eyikkIIYSQN9DX18ehQ4fQo0cPuePHjh2Dt7c3nj9/ziYYz6qrq7F8+XLs2rULeXl5suE/tR4/fswoGeGLWAeaAdLX+/79+5Geng4AcHZ2hre3N1RVVRknI4SIARUBCRGgXr16YdKkSQoXVjExMdiwYQOOHz/OJhghhBBC3sjHxwcnT57EsmXL4OHhAUDa2mHWrFno2rUroqKiGCfkR0hICDZt2oTAwEB89913+Pbbb5Gbm4v9+/cjJCREdJNixUCsA81u3bqF/v37Iz8/X9b3MyMjA1ZWVjh8+DANOiOE8I6KgIQIkI6ODtLS0uq8sGrdujVKS0sZJSOEEELIm5SWliIoKAhbtmxBZWUlAEBNTQ0TJ07EkiVLoKuryzghP+zt7bFy5UoMGDAA+vr6uHTpkuzYuXPnEBMTwzoiqWd+fn5QV1dX6N0bFBSEsrIyrFmzhlEyfvXv3x8cx+GXX35BgwYNAACFhYUYPXo0VFRUcPjwYcYJCSFCR0VAQgTIyckJAwcORHh4uNzx2bNn48CBA8jIyGCUjBBCCCH/pKSkBFlZWQCkBTKhFv9q6erqIj09HdbW1rCwsMDhw4fh7u6O7OxstGnTBk+fPmUdkdQzPz8/REdHw8rKqs6BZq8O+hLSkC9dXV2cO3cOrq6ucsfT0tLQpUsXFBcXM0pGCBELmg5MiAAtX74c//nPf/DHH3+gQ4cOAIALFy4gMzMTe/fuZZyOEEIIIW+jq6sLNzc31jGUpkmTJrh37x6sra1hb2+P2NhYuLu7IykpCZqamqzjER5cvXoV7u7uACAreJuYmMDExARXr16VPe9dh959KDQ1Nevs7VlcXAwNDQ0GiQghYkMrAQkRqDt37mDt2rWypsMtWrTAlClTYGVlxTgZIYQQQshLwcHBMDAwwDfffIOdO3di9OjRsLGxQV5eHgICArBo0SLWEQmpFz4+PkhJScHmzZvl+n76+vqibdu2iIyMZBuQECJ4VAQkRKBOnjyJdevWITs7G7t374alpSW2bdsGW1tbfPTRR6zjEUIIIYTU6dy5czhz5gwcHR3x6aefso5DSL0pKirC2LFjcejQIdmW58rKSgwcOBCRkZEwNDRknJAQInQqrAMQQurfnj170KdPH+jo6CA1NRUvXrwAADx9+hQ//PAD43SEEEIIIS/9+OOP2LJli+z7jh07YubMmSgoKMDixYsZJiOkfhkZGeHAgQPIzMzEb7/9ht9++w2ZmZnYt28fFQAJIUpBRUBCBGjBggVYt24dNm7cKNdYuUuXLkhJSWGYjBBCCCFE3vr169G8eXOF487Ozli3bh2DRITwZ/PmzRg0aBCGDh2KoUOHYtCgQdi0aRPrWIQQkaDBIIQIUEZGBrp166Zw3NDQEEVFRcoPRAghhBDyBvfv34eFhYXCcVNTU9y7d49BIkL4ERISgoiICPj5+aFTp04AgLNnzyIgIAB5eXkICwtjnJAQInRUBCREgBo1aoRbt27BxsZG7vipU6dgZ2fHJhQhhBBCSB2srKxw+vRp2Nrayh0/ffo0GjduzCgVIfVv7dq12LhxI0aMGCE75u3tDTc3N/j5+VERkBDCOyoCEiJAvr6+8Pf3x5YtWyCRSHD37l2cPXsWQUFBmDt3Lut4hBBCCCEyvr6+mDFjBiorK+Hp6QkASEhIwOzZsxEYGMg4HSH1p7KyEu3atVM43rZtW1RVVTFIRAgRG5oOTIgAcRyHH374AT/++CNKS0sBAJqamggKCsL8+fMZpyOEEEIIeYnjOAQHB2PlypWoqKgAAGhpaWHOnDkICQlhnI6Q+uPn5wd1dXVERETIHQ8KCkJZWRnWrFnDKBkhRCyoCEiIgFVUVODWrVsoLi5Gy5YtoaenxzoSIYQQQkidiouLkZ6eDm1tbTg6OkJTU5N1JELqlZ+fH6Kjo2FlZYWOHTsCAM6fP4+8vDz4+PjIDfR7vVBICCH1gYqAhBBCCCGEEEIIz3r27PlOz5NIJDh69CjPaQghYkRFQEIIIYQQQgghhBBCBE6FdQBCCCGEEEIIIYQQQgi/qAhICCGEEEIIIYQQQojAURGQEEIIIYQQQgghhBCBoyIgIYQQQgghhBBCCCECR0VAQgghhBBCCCGEEEIEjoqAhBBCCCGEEEIIIYQIHBUBCSGEEEIIIYQQQggRuP8DMaBxwPGW3CAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1700x1700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# split labels and features\n",
    "y = df[\"Target\"].copy()\n",
    "x = df.drop(\"Target\", axis=1)\n",
    "\n",
    "# split test and train data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.33, random_state = 42)\n",
    "\n",
    "plt.figure(figsize=(17,17))\n",
    "sns.heatmap(data = df.corr(), annot=True, cmap=\"YlOrBr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rYaiPL1plrDm"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"1\">\n",
    "# <font color=\"#800080\" size=6>**1. Majority Voting** </font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E5LZv8WxAESl"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "LpEmhZsmAESl"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Create models\n",
    "log_clf = LogisticRegression(solver='lbfgs', max_iter=30000, random_state = 42)\n",
    "dt_clf = DecisionTreeClassifier(random_state = 42)\n",
    "svm_clf = SVC(random_state = 42)\n",
    "hard_voting_clf = VotingClassifier(estimators=[('lr', log_clf), ('dt', dt_clf), ('svc', svm_clf)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gSvKw0pdAESm"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "id": "wTn5oEryrDuh",
    "outputId": "9e0c0c60-5d39-4ee9-9967-e256582e63ae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;,\n",
       "                              LogisticRegression(max_iter=30000,\n",
       "                                                 random_state=42)),\n",
       "                             (&#x27;dt&#x27;, DecisionTreeClassifier(random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;,\n",
       "                              LogisticRegression(max_iter=30000,\n",
       "                                                 random_state=42)),\n",
       "                             (&#x27;dt&#x27;, DecisionTreeClassifier(random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(random_state=42))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=30000, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>dt</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('lr',\n",
       "                              LogisticRegression(max_iter=30000,\n",
       "                                                 random_state=42)),\n",
       "                             ('dt', DecisionTreeClassifier(random_state=42)),\n",
       "                             ('svc', SVC(random_state=42))])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "results = {}\n",
    "for clf in (dt_clf, svm_clf, log_clf):\n",
    "    clf.fit(x_train, y_train)\n",
    "    y_pred = clf.predict(x_test)\n",
    "    results[clf.__class__.__name__] = accuracy_score(y_test, y_pred)\n",
    "\n",
    "hard_voting_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DCicBNv3AESm"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "vYem4ThqAESl"
   },
   "outputs": [],
   "source": [
    "y_pred = hard_voting_clf.predict(x_test)\n",
    "results[\"HardVotingClassifier\"] = accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rpEZrgzGAESn"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Visualization**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 599
    },
    "id": "7ouxoEg6AESn",
    "outputId": "c16398c9-c3c4-4ec2-d2cb-3d443bf7d55e"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 7))\n",
    "ax = sns.barplot(x=list(results.keys()), y=list(results.values()), palette=\"crest\")\n",
    "ax.set_ylim([min(list(results.values()))-0.002, max(list(results.values()))+0.005])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zOUaq_zLrDun"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"2\">\n",
    "# <font color=\"#800080\" size=6>**2. Bagging** </font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "14oWGLnbAl1y"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "ozKb37LXAl1y"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=2000, max_samples=400, bootstrap=True, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osVXNTRxAl1y"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "zXCt34qrAl1z",
    "outputId": "3722f51a-37ca-46b4-9310-71f663f2d688"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=400,\n",
       "                  n_estimators=2000, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=400,\n",
       "                  n_estimators=2000, n_jobs=-1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=400,\n",
       "                  n_estimators=2000, n_jobs=-1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SULBwRA6Al10"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8Iel5jWADA_0"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Prediction </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cellView": "form",
    "id": "ib8Or3t8Al11"
   },
   "outputs": [],
   "source": [
    "y_pred = bag_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mKYauB0MrDuw"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jIrsI6dEAl11",
    "outputId": "3e0e7142-114a-4c10-f352-411ed1206170"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8972520107238606"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qdiogb1zrDuz"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Visualization**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "EmzQwRudrDu0",
    "outputId": "022c45a1-7492-4f0e-9e47-04d6d8bdb8f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def show_confusion_matrix(y_test, y_pred):\n",
    "  clr_plt = sns.light_palette(\"#FF9F29\", reverse=True, as_cmap=True)\n",
    "  cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "  sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, fmt='.2%',cmap=clr_plt)\n",
    "  plt.ylabel('Predicted label')\n",
    "  plt.xlabel('True Label')\n",
    "  plt.show\n",
    "\n",
    "show_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BG6JDKXAKthv"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"2-1\">\n",
    "## <font color=\"#800080\" size=6>**2-1. Out Of Bag Validation**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HGGBO53orDu2",
    "outputId": "1eb63db6-36d8-4187-b6c5-04eeaa3f3a8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOB Score: 0.9037667954177808\n",
      "Accuracy Score : 0.9044235924932976\n"
     ]
    }
   ],
   "source": [
    "bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=2000, bootstrap=True, n_jobs=-1, oob_score=True)\n",
    "bag_clf.fit(x_train, y_train)\n",
    "print(\"OOB Score: \" + str(bag_clf.oob_score_))\n",
    "print(\"Accuracy Score : \"+ str(accuracy_score(y_test, bag_clf.predict(x_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NdeIBJnLrDu3"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"3\">\n",
    "# <font color=\"#800080\" size=6>**3. Pasting** </font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VQ5QM-42DA_1"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "Cb9U5AFnDA_2"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=2000, max_samples=400, bootstrap=False, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UYJC961EDA_2"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "vx27gm_9DA_2",
    "outputId": "b91130bc-2d21-4c85-ab8b-e2a06744dfa9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(bootstrap=False, estimator=DecisionTreeClassifier(),\n",
       "                  max_samples=400, n_estimators=2000, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(bootstrap=False, estimator=DecisionTreeClassifier(),\n",
       "                  max_samples=400, n_estimators=2000, n_jobs=-1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(bootstrap=False, estimator=DecisionTreeClassifier(),\n",
       "                  max_samples=400, n_estimators=2000, n_jobs=-1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sChFVxAErDu8"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vORapzaoJJug"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Prediction </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "uFmdK69CrDu-"
   },
   "outputs": [],
   "source": [
    "y_pred = bag_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zT_XKre5rDvA"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jIeG9zh5rDvB",
    "outputId": "8842fd02-bdcc-4f7c-f9f8-9b2a121a74db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8959785522788204"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vGCKqHATrDvD"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Visualization**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "GBZX97QmrDvE",
    "outputId": "36979891-93a1-4b2e-8d6f-7a3739c35849"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kOsXcN2HrDvG"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"4\">\n",
    "# <font color=\"#800080\" size=6>**4. Random Forest** </font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "thSJHE-lrDvH"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "f9zaHrCOrDvH"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KpSx-BswrDvI"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "nM04kQ0urDvJ",
    "outputId": "b308a1f2-72c1-42f3-9780-c57f542e1b47"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_leaf_nodes=16, n_estimators=500, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_leaf_nodes=16, n_estimators=500, n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_leaf_nodes=16, n_estimators=500, n_jobs=-1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LaDe2LeyrDvK"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4qTCY6wrDvL"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Prediction </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "j55Q2J9TrDvM"
   },
   "outputs": [],
   "source": [
    "y_pred_rf = rnd_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DgJlQWw0rDvN"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x1wcAfA8rDvO",
    "outputId": "3d7554c2-6c84-4761-81e8-c285f1be32d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8959785522788204"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3pZCWtkeo2Q_"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"5\">\n",
    "# <font color=\"#800080\" size=6>**5. Extra-Trees**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I3TA7qSXJJug"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "VrZ1dQ-PrDvR"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "ex_tree_clf = ExtraTreesClassifier(n_estimators=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NaigOiuRrDvT"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "aFJjedVurDvT",
    "outputId": "faaa3083-ac09-4ca2-8cb5-237ccf308640"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesClassifier(n_estimators=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(n_estimators=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesClassifier(n_estimators=500)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex_tree_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t5ww6IbRrDvU"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AIOVEljSrDvV"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Prediction </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "oipd4RysJJug"
   },
   "outputs": [],
   "source": [
    "y_pred_rf = ex_tree_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2JNOCPY3JJuh"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0ppzbAv8JJuh",
    "outputId": "f426e63a-92a8-451c-f844-8b7342ee9211"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8959785522788204"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KKi8dItdrDvZ"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6\">\n",
    "# <font color=\"#800080\" size=6>**6. Boosting**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kny1ud_NrDva"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-1\">\n",
    "## <font color=\"#800080\" size=6>**6-1. AdaBoost**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RnJ3PbxLKkyu"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-1-1\">\n",
    "### <font color=\"#800080\" size=5>**6-1-1. SAMME**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "950JsRNjMAny"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "rpQN1W-iMAnz"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada_clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200, algorithm=\"SAMME\", learning_rate=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iOPf8PsJMHZ4"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "dhd4xKrtMHxd",
    "outputId": "365a94e9-2412-4b67-e200-dfe4ec00b936"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(algorithm=&#x27;SAMME&#x27;,\n",
       "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(algorithm=&#x27;SAMME&#x27;,\n",
       "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME',\n",
       "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a6xyh9qQMNUh"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4VpbP9QhrDvi"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Predictoin </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "sb4uu0s5rDvi"
   },
   "outputs": [],
   "source": [
    "y_pred = ada_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1I9_Bxd7rDvk"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8mliBzl4rDvl",
    "outputId": "9ab5542c-2a51-4385-e17a-cc6659379c22"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8948391420911528"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gs_qPMs9rDvm"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-1-2\">\n",
    "### <font color=\"#800080\" size=5>**6-1-2. SAMME.R**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MZ7PElwtrDvn"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "SLcU84ZrrDvo"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada_clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200, algorithm=\"SAMME.R\", learning_rate=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "41OINF02rDvp"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "IYpadcPZrDvq",
    "outputId": "95cf1a0a-f407-4f04-d54e-8058195e67c2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QPHDSU8IrDvr"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6sCnZ8ZBrDvs"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Predictoin </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "l8PshuL6rDvs"
   },
   "outputs": [],
   "source": [
    "y_pred = ada_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FZBi_u1drDvt"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IC-MOPdXrDvu",
    "outputId": "933541b1-45ca-4fd2-9941-43c99428d817"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8989276139410187"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cx1CdeB_rDvv"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-2\">\n",
    "## <font color=\"#800080\" size=6>**6-2. Gradient Boost**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HBT59RLpIyUY"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-2-1\">\n",
    "### <font color=\"#800080\" size=5>**6-2-1. Without Scikit-Learn**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YK6GSHx0rDvx"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 95
    },
    "id": "TS8rznn5rDvy",
    "outputId": "f26c8329-a993-441b-f002-d489c742d07b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cl1 = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_cl1.fit(x_train, y_train)\n",
    "\n",
    "y2 = y_train - tree_cl1.predict(x_train)\n",
    "tree_cl2 = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_cl2.fit(x_train, y2)\n",
    "\n",
    "y3 = y2 - tree_cl2.predict(x_train)\n",
    "tree_cl3 = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_cl3.fit(x_train, y3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B2iWsmxtrDv0"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qdNqCaagrDv1"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Predictoin </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "csubNK8IrDv4"
   },
   "outputs": [],
   "source": [
    "y_pred = sum(tree.predict(x_test) for tree in (tree_cl1, tree_cl2, tree_cl3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "As5ULNfmrDv5"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qc3pfjbdrDv7",
    "outputId": "255ef0c8-46f7-4a01-b834-389806df7215"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8880026809651475"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pEUKOtD4rDv8"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-2-2\">\n",
    "### <font color=\"#800080\" size=5>**6-2-2. with Scikit-Learn**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KXTyGvRErDv9"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "UQ5eY4bBrDv9"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gbc = GradientBoostingClassifier(max_depth=2, n_estimators=5, random_state=42, learning_rate=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QS3D5gk4rDv-"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 92
    },
    "id": "ojrKFMh1rDv_",
    "outputId": "12f2ef85-9d02-4f40-ca24-6d46505b13a1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=1.0, max_depth=2, n_estimators=5,\n",
       "                           random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=1.0, max_depth=2, n_estimators=5,\n",
       "                           random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=1.0, max_depth=2, n_estimators=5,\n",
       "                           random_state=42)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YlV29n5UrDwA"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fhIaNVE3rDwB"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Predictoin </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "MN5F4MEirDwC"
   },
   "outputs": [],
   "source": [
    "y_pred = gbc.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rZkSEynCrDwD"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1IBrtA2erDwF",
    "outputId": "157ef105-ab89-4e60-e759-72bc31aa57f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8939678284182305"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1TgrioWuqBHB"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-3\">\n",
    "## <font color=\"#800080\" size=6>**6-3. Early Stopping**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DRvYLaqwrDwG"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-3-1\">\n",
    "### <font color=\"#800080\" size=5>**6-3-1. Method**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DXDtIWVI8y1_"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Methodology**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DE3-Zg6i9L-E"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Implementation </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "8WOdD8wi8y2B"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "result={}\n",
    "for count_of_estimators in range(1, 70):\n",
    "  gbc = GradientBoostingClassifier(max_depth=2, n_estimators=count_of_estimators, random_state=42, learning_rate=1.0)\n",
    "  gbc.fit(x_train, y_train)\n",
    "  y_pred = gbc.predict(x_test)\n",
    "  val_error = mean_squared_error(y_test, y_pred)\n",
    "  result[count_of_estimators]=val_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FE70nOX58y2C"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Visualization </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 627
    },
    "id": "fmoX4Tf5qiIp",
    "outputId": "5485c474-967d-4556-e6fb-fc9d59f8459b"
   },
   "outputs": [
    {
     "data": {
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9tFsbt1VI6ggWLz5rXNier7K2RT959AO1tvu0aNYYLbukwJQJ9XXudr26pVRvfFSmlnafCZUelRBr1+3XztSkcSmmXhcAAAAAEHmpqQmy2U69HV9UBZmSVFhYqJ/85Cf66KOPlJCQoKuuukorV66Uw+EInnPjjTeqrKxMr732WvDYD37wAz3zzDM9rjd37lytWbMm+HlVVZV++tOf6q233pLdbtfFF1+sH/3oR0pMTAy5ZoJM9EUgYOiFt0v0/KZiGYaUnRav266arnGjQ3/v9YVhGHpp8yE9+cZ+GYZ0Wk6yVnx2hpITHKd+MAaNYRj622v79fL7hyRJV5+XpyWd08PN1NLm008f+0CVtS06bWyyvveFObL34X8W/dHm8emt7RV6/aMy1Td5Bnw9fyAgjzcgZ4xN37p+Zr+7RwEAAAAA0W3IBplDEUEm+mPPwTr98fmdqm/yyG6z6gufPl2fmj3G9MBKkrw+vx7dsEdvf1wpSTpvZrZu+ExB1AwdQneGYeiFTSV69q1iSdIlc8fr+gsmmvbeCAQM/e/ft2t7YY1GJTl115fPHhKBdrvXr/v/vl07S+rksFv1H9fN1JTc1EiXBQAAAAAwCUHmICLIRH81tnj08D92aXthjSTprIIMfWpOjsakJyg5wWFKcFXf1K77n96hovJGWS0WLb3oNF105tiwBKYw18vvH9K6V/dJks6fPUY3fqbAlME5f3+zUP9454Bi7Fb98IYzlJvlGvA1B4vX59fqp3fo46JaxdituuPamZqWR5gJAAAAAMMBQeYgIshEKAKGoVfeP6Sn3iiUP3D0P8N4p11j0hM0Jj1eY9ISNCY9QdlpCUp1OfscQhZXNGr137ervsmjhFi7brt6uqbRwTak/Htbuf6yYbcMQ5o7ZbS+esXUAS0Bf++TKv3x+Z2SpFuXTNX8CA2cGgivL6DfPbND2wtrZLdZdfu1MzQjPy3SZQEAAAAABoggcxARZGIgiisatf7dAyqtbtbhuhad6L9Ip8OmMWnHhJvpHR/Tk2NlPSbgfGdnpR5Zv1s+f0DZafG647qZyhwVP0ivBmZ6f/dhPfj8TvkDhmZOTNM3rp4uR0z/B0QdqHTrnse3yOML6NJ543X9BaeFodrB4fUF9MBzH+ujfUdkt1m04poZmnVaeqTLQieP16+Pi2v1wZ7D2l/aoIyUOOVmJykvy6Xc7CSluWLpCgcAAADQA0HmICLIhFm8Pr+qaltVXtOs8iOdf2paVFXb0q1r81gOu1VZafEak54gq8US3A9z1sQ03XrlNMU57YP5EmCy7YU1+t0zO+T1BTR5fIpuv3Zmv76njc0e/dej76u2sV0z8tP0H9fNNGWZeiT5/AH98bmd2rK3WjarRd+4errmTMoYtOdvbfdpR1HHPqN52S7ThyUNNe0ev7YX1eiD3Ye1vbBG7V7/Cc9Nio9RbpZLuVlJHQFntkspic5BrBYAAABANCLIHEQEmQg3nz+gw3WtncFmV8jZosraFvn8gR7nX75ggq45L3/IB1bosOdgnX771Ha1efzKy3Zp5edmKTEu5pSP8/kD+vn/faR9pQ3KTI3Xj286U/Gxp37cUODzB/SnFz7R+7sPy2a16LarpunMgtFhfU7DMPT+7sP6v1f3qaFzGrvDbtXEnGQVjEtRwfgU5Y9xKcbe/67Zoaa13adthUe0ZXe1dhTVyOM7+nMozRWrsyZnaFpeqmob21VS0ajiSrdKDzf1+g8yKYkO5WV3hZsdH5Pi+z+EKmAYamnzyd3ikbvF2/Gnteu2R00tXnl9PX9ehsJmsygxLkZJ8Q4lxXd+jIsJ3k6MixmRP399/oD2lzZoR3GNGps9ykqN79wqJUEZyXEj8msCAACAviHIHEQEmYgUfyCgI/VtwYDzSEObZk5M05zTB687DYOjuKJRv/rbVjW3+ZSTkaDvfH72KTvZHtuwW29sLVec06b/76azlJ2WMEjVDg5/IKCHXtyldz+pktVi0a1XTtXcKZlhea6q2hY9/spe7SyulSSlupzyeANqavV2O89usyp/jCsYbE7MSZYzhO0AolFLm1cf7TuiLXuq9XFxjXz+o78+jE6J05mTM3RWwWjlZiX1unzc6/OrtLpZxRWNKqlwq7iyUeVHmnvdTiM9OVa52S7lZSVpQuf13C0eNbV6g8Fk8GPnsaYWrwJR8iuNRVJCV7B5TOCZeFzgmZLoUHZnN/1QVedu146iGu0orNEnB2rV2t57R67dZu0MNjvDzc5tUkaPihvxXc0AAAAgyBxUBJkABkNZdZN+8betamjyaHRKnO5cOlvpKXG9nvv6R2Va89IeWST9x/UzNXPi8NxHMhAw9NA/dumdnZWyWKSvXWHuICOvz6/17x7UP945IJ8/ILvNqssXTNBl88fLbrOq/Eiz9hyq156D9dpzqF6NzZ5uj7dZLcrLdqlgfIoKxnUEm0Npu4emVq8+2lutD/ZU65OS2m4dlVmp8TqrM7wcNzoxpL0v2z1+Hahyq6TSrZLKRhVXuFVV2zKgmuOcNiXFHe2UTIzvChQdcjrMCZW9voCaWo/p/DwmWG1u8/XrWqOSnDpzUobOmjxap+UkR33Xos8fUGFZg3YU1WpHUY0OHW7qdn9SfIym56UpMzVOVbUtKj/Sooqa5m5du8eyWS3KTI3v2AM6/eiAu6zUuBHR3QwAAIAOBJmDiCATwGA5XN+qX/zfRzrS0KZRSU7duXR2j07LPQfr9It1W+UPGLruUxN12fwJEap2cAQChv7yz916a0eFLBbplsun6Jzp2QO+7s7iWq15eY8O17VKkqblpeqGiycpM7X34VmGYaiytkV7D9UHw806d3u3c6wWiyZkJalgXIomjU/RpLHJUbfcv7HZow/3VuuDPYe1+0B9ty7HnIwEnVUwWmcVZGhMekJYBve0tPl0oLJRJZVuFVe6dbDKLavFcnQJ9zHBZPdjHUu6Y+yR7e7zBwJqavV17xo9pnu06ZjbR+rbuu0pmpzg0BkFHeHwpHHJslmjo1Oxvulo1+XOkjq1th8Nay2S8sa4NCM/TTMnpmlCVlKPDtOAYaimoa3H9ijlNc1q9/TewWmxdHT6jhudqMVzx2tiTnI4XyIAAAAijCBzEBFkAhhMde52/WLdR6qoaVFSfIy+8/nZGp+ZJEk60tCq//rLB2pq9WrulNFafuW0ETElOmAYemzDHm3cVi6LpC9fNlnnzRwT0rXqm9q17tV92rzrsCQpOdGhL1x0us6ePLpfX0vDMFTd0KY9B+u0t7Nj80hDW7dzLJJindHVddbW7texvxiMH52oMyd3hJfDbXuCSPP6/NpZUqctuw/ro31H1HJMQJgUH6MzJnWEmgXjUwZ1+bU/EFBhWWMwvDx4XNdlYlyMpuelasbENE3LS5UrhD1NpY7/Rurc7ccMt+sIOMuONHcLSyVpwbRMXXv+RKW6YkN+XdGkscWjxiaPHDFWOWJscthtcjqsURNeAwAADDaCzEFEkAlgsDW2ePTrv23TgSq34px2rbx+lsaNTtQ9j2/RwcNNmpCZpB/ccMaw2Z+xLwKGobUv79XrH5VJkpZdUqDzZ+f0/fEBQ69/VKanNxaqtd0vi0W66Iyxuvq8fMXHmrMcvKahTXsO1QWXond1e0ab3KwknTV5tM4syFDmqN47UGEunz+gXQfq9MHuw/pwb3W3JeoJsXbNOT1DZ03O0NTcVFNDzXavXzUNbTrS0KYjDa3ac7BeO4tru4WqkpSXnaQZ+WmaMTFNeVmusC6BNwxDDc0elR9p1rs7q7RpR4UMSY4Yqy6bN0GL540fcj/b6tztx3Rr16mipvctFGxWi5wxNjlirJ0fbcHPO8JOmxz2Y++zKtZpV2pSrNKTY5WWHKuEWPuI+AcsAAAwvBBkDiKCTACR0NLm02+f2qZ9pQ1yxFiVl+XSnkP1csXH6MfLzlZa8vDoXOoPwzD013/t06tbSiVJN35mki44Y+wpH1dc0ajHXtqjA5VuSR2hzU2LJ2tCVlJY621s9qjV0789FcMt1mFXckJoHXYwh88f0J5D9dqy+7C27K2Wu+XoUKk4p12zT0vXWZMzND0v9ZT7SLa2+3SkoU01DW2qaewIK7uCy5rGtm7XPlZCrF3T8lI1c2KapuelyRXB90RJZaP++q992l/aIKlj2Nb1nzpNc6f0r0t6MPXlHy2S4mPk9QXU7vX3OvQqVLEOm9KSY5Xu6gg205PjOj/GKs0Vq6T4mKj9ugEAgJGLIHMQEWQCiJR2r1+/e3qHPu6cpm2zWvTdL8zRpHEpkS0sggzD0N9e26+X3z8kSfrip0/Xp88a1+u5LW1e/X1jkd74sEyGOkKi687P1/mzc6J+6ApGhkDA0L7Sen2wu1of7D2shqajA6WcDptmTUzTGZMyZLNaVdPQqiONnaFlZ1h5fGdlb+KcNqW54pSeHKuxoxM1Mz9NeWOSomqZs2EYen/3YT35+n7VNHbsPXtaTrK+8OnTlZftinhtfdlGYlxmogrGjVLB+BRNGpeixLiY4ON9fkPtXr88Xn/nx46A0+Pzq90T6Ph47PFjbre0eVXT2K6ahlY1niCYPpbDblVaZ6jZ1cWZlhyrjOQ45WZH1/cdAACMHASZg4ggE0AkeX0BPfSPT7RlT7VuXFygRbNC2xtyODEMQ0+9Uah/vndQkrT0wtP0mbnju93/3idVWvfa/uCk8fnTMvX5C05TcqIzIjUDpxIwDBWWNXSEmnsO9xgmdSIJsfYeXXldAVZ6cmzUDZw6GY/Xrw2bD2r9uwfk8XZMQl84PUufPX+iRiUNzn+7XYO99hyqDwaXJxzsNT5FBeNSdPogDfZq9/pV29jWrev22Nv17nad7Bf/lESHFs0ao/Nn5wza1xMAAEAiyBxUBJkAokG71z/k9o0LJ8Mw9PTGIv3jnQOSpOsvmKhL501QRU2zHn95r3YdqJMkZaXG68bPTNKU3NRIlgv0S8AwVFzRqC17qvVxUY1i7LZeg8o0V6zinOb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\n",
      "text/plain": [
       "<Figure size 1600x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 7))\n",
    "sns.set(style='dark',)\n",
    "ax = sns.lineplot(x=list(result.keys()), y=list(result.values()))\n",
    "plt.xlabel(\"Number of estimators\")\n",
    "plt.ylabel(\"Validation error\")\n",
    "ax.set_xlim([1, max(list(result.keys()))])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6mjmvf10rDwK"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Result </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "goBVvXwbrDwL",
    "outputId": "29f070de-ab27-4f87-fa12-6880fbba7974"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best number for estimators is: 65\n",
      "The minimum value for validation error is: 0.09631367292225201\n"
     ]
    }
   ],
   "source": [
    "print(\"The best number for estimators is: \"+ str(list(result.values()).index(min(list(result.values())))+1))\n",
    "print(\"The minimum value for validation error is: \"+ str(min(list(result.values()))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WR4L3aExrDwM"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-3-2\">\n",
    "### <font color=\"#800080\" size=5>**6-3-2. Find Min Validation Error**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DiHjLHa0rDwN",
    "outputId": "6d37bbcf-7ad5-47c9-f552-30e1cdf7e214"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best n_stimators value is: 10\n"
     ]
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(max_depth=2, warm_start = True, random_state=42, learning_rate=1.0)\n",
    "min_val_error = float(\"inf\")\n",
    "error_going_up = 0\n",
    "number_of_steps_to_checks=5\n",
    "for n_estimators in range(1, 70):\n",
    "    gbc.n_estimators = n_estimators\n",
    "    gbc.fit(x_train, y_train)\n",
    "    y_pred = gbc.predict(x_test)\n",
    "    val_error = mean_squared_error(y_test, y_pred)\n",
    "    if val_error < min_val_error:\n",
    "        min_val_error = val_error\n",
    "        error_going_up = 0\n",
    "    else:\n",
    "        error_going_up += 1\n",
    "        if error_going_up == 5:\n",
    "            break\n",
    "gbc.n_estimators = gbc.n_estimators - number_of_steps_to_checks\n",
    "print(\"The best n_stimators value is: \" + str(gbc.n_estimators))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wINoG1o6rDwO",
    "outputId": "c03a7795-29e1-4b15-fec1-dd9a634d9dc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum value for validation error according the above algorithm is: 0.09711796246648793\n"
     ]
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(max_depth=2, random_state=42, learning_rate=1.0)\n",
    "gbc.fit(x_train, y_train)\n",
    "y_pred = gbc.predict(x_test)\n",
    "val_error = mean_squared_error(y_test, y_pred)\n",
    "print(\"The minimum value for validation error according the above algorithm is: \" + str(val_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HgKzXBD1rDwP"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-4\">\n",
    "## <font color=\"#800080\" size=6>**6-4. XGBoost**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JjFaEHf7rDwQ",
    "outputId": "5d8aa4d6-cd3d-460e-fb8a-99e5584f23bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (1.7.6)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.23.5)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fnaXSgAZ-RoF"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "DOpq2MU2rDwS"
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "XG_classifier = XGBClassifier(random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xrLf6QyUrDwS"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "id": "7p283CvArDwT",
    "outputId": "16ee47c1-f093-45e9-e6bc-ae5d573d82fd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XG_classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XEjr7eSZrDwV"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "ubKA5NN1rDwW"
   },
   "outputs": [],
   "source": [
    "y_pred = XG_classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SESrkP7WrDwX",
    "outputId": "a2f6f120-abae-4dbe-b16d-3c82ef2aac1d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9070375335120644"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FfiTeR9DrDwY"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Visualization**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "dhbzsJ-XrDwZ",
    "outputId": "48bbfdd6-a5fd-4340-cb2f-2bbb8a9a08da"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tcKluxdprDwZ"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-5\">\n",
    "## <font color=\"#800080\" size=6>**6-5. CatBoost**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x666lkBprDwa",
    "outputId": "deb51ab5-9acd-4b19-909d-f40d4daa4697"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting catboost\n",
      "  Downloading catboost-1.2.1-cp310-cp310-manylinux2014_x86_64.whl (98.7 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
      "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n",
      "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.2)\n",
      "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.3.post1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.42.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.1)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n",
      "Installing collected packages: catboost\n",
      "Successfully installed catboost-1.2.1\n"
     ]
    }
   ],
   "source": [
    "!pip install catboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jP2M6fI6rDwb"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"6-5-1\">\n",
    "### <font color=\"#800080\" size=5>**6-5-1. Categorical Features**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_0fyugTArDwc"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Dataset**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5IGZwfLK1cOi"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Load Dataset </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "tQUhwqgUrDwd"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../Files/bank-full.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qn63yKvCrDwe"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Preprocessing Dataset </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "zibGWNCprDwe"
   },
   "outputs": [],
   "source": [
    "colums_have_unknow=['job', 'marital', 'education', 'default', 'housing', 'loan']\n",
    "for colum_has_unknow in colums_have_unknow:\n",
    "    df.drop(df[df[colum_has_unknow] == 'unknow'].index, inplace = True)\n",
    "\n",
    "y = df[\"Target\"].copy()\n",
    "x = df.drop(\"Target\", axis=1)\n",
    "cat_features = list(range(0, x.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OuleUT7a-aLd"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "u16ILCH8rDwg"
   },
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "\n",
    "clf = CatBoostClassifier(iterations=5, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aesC14KKrDwh"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Uaw8IViW-aLd",
    "outputId": "6dbc5959-05cb-424c-c4cc-f924c45e7fe3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7d885db7c610>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train, y_train, cat_features=cat_features,  verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ac27W-T_rDwi"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cSG12v8VrDwj"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Prediction </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "1HV_CAMJrDwl"
   },
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7zUWx99prDwl"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KoOSe-rJrDwm",
    "outputId": "17d8913c-55a6-423b-d6b4-0a3291d3f8d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8922922252010724"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v3vX2AMI-jCL"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Visualization**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "xQ8RwAJg-Nlp",
    "outputId": "048ee2a3-2872-47df-cc13-d6b374b449bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NB7H40DRqdBB"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"7\">\n",
    "# <font color=\"#800080\" size=6>**7. Stacking**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rsLZgPQUrDwp"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Model**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "4cP4MucdrDwq"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "rf_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(random_state=42)\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=3)\n",
    "stacking_clf = StackingClassifier(estimators=[('rf', rf_clf), ('svc', svm_clf), ('knn', knn_clf)], final_estimator=LogisticRegression())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C3g1kcvYrDwr"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Train**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163
    },
    "id": "2BzQXlqsrDws",
    "outputId": "108b12b0-0401-40c5-c194-ce821e6df6d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;rf&#x27;,\n",
       "                                RandomForestClassifier(n_estimators=10,\n",
       "                                                       random_state=42)),\n",
       "                               (&#x27;svc&#x27;, SVC(random_state=42)),\n",
       "                               (&#x27;knn&#x27;, KNeighborsClassifier(n_neighbors=3))],\n",
       "                   final_estimator=LogisticRegression())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;rf&#x27;,\n",
       "                                RandomForestClassifier(n_estimators=10,\n",
       "                                                       random_state=42)),\n",
       "                               (&#x27;svc&#x27;, SVC(random_state=42)),\n",
       "                               (&#x27;knn&#x27;, KNeighborsClassifier(n_neighbors=3))],\n",
       "                   final_estimator=LogisticRegression())</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=10, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>knn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingClassifier(estimators=[('rf',\n",
       "                                RandomForestClassifier(n_estimators=10,\n",
       "                                                       random_state=42)),\n",
       "                               ('svc', SVC(random_state=42)),\n",
       "                               ('knn', KNeighborsClassifier(n_neighbors=3))],\n",
       "                   final_estimator=LogisticRegression())"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacking_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hg10RDZBrDwt"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Evaluation**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nB-5lmDtrDwu"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Prediction </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "j_E34jqurDwv"
   },
   "outputs": [],
   "source": [
    "y_pred = stacking_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uZn2wYXlrDwv"
   },
   "source": [
    "##### <font color=\"#008080\" face=\"Times New Roman\" size=4> - Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2iJWmiS4rDww",
    "outputId": "5a5c56bf-48d5-4da6-896a-ae650f84b283"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8998659517426274"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8Irp5LsrrDwx"
   },
   "source": [
    "#### <font color=\"#008080\" face=\"Times New Roman\" size=4>**Visualization**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "iD3aphdurDwy",
    "outputId": "43ea2bb6-31f8-4b80-b0fa-a750df449bd5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y9SBiD-1rDwz"
   },
   "source": [
    "<font face=\"Times New Roman\"><div id=\"7-1\">\n",
    "## <font color=\"#800080\" size=6>**7-1. Multi-layer Stacking**</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "4G4XzXZUrDw0"
   },
   "outputs": [],
   "source": [
    "# Note: To be able to run the two last cells, the result should be stored in the `y_pred` variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QWbcJGdsrDw2"
   },
   "outputs": [],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d1RbGXGWrDw2"
   },
   "outputs": [],
   "source": [
    "show_confusion_matrix(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "950JsRNjMAny",
    "iOPf8PsJMHZ4",
    "HBT59RLpIyUY",
    "fnaXSgAZ-RoF",
    "OuleUT7a-aLd",
    "v3vX2AMI-jCL"
   ],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "local-venv",
   "language": "python",
   "name": "local-venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
